Strategies, models, and algorithms facilitating such models are explored to provide transportatio... more Strategies, models, and algorithms facilitating such models are explored to provide transportation network managers and planners with more flexibility under uncertainty. Network design problems with non-stationary stochastic OD demand are formulated as real option investment problems and dynamic programming solution methodologies are used to obtain the value of flexibility to defer and re-design a network. The design premium is shown to reflect the opportunity cost of committing to a “preferred alternative” in transportation planning. Both network option and link option design problems are proposed with solution algorithms and tested on the classical Sioux Falls, SD network. Results indicate that allowing individual links to be deferred can have significant option value. A resource relocation model using non-stationary stochastic variables as chance constraints is proposed. The model is applied to air tanker relocation for initial attack of wildfires in California, and results show that the flexibility to switch locations with non-stationary stochastic variables providing 3-day or 7-day forecasts is more cost-effective than relocations without forecasting. Due to the computational costs of these more complex network models, a faster converging heuristic based on radial basis functions is evaluated for continuous network design problems for the Anaheim, CA network with a 31-dimensional decision variable. The algorithm is further modified and then proven to converge for multi-objective problems. Compared to other popular multi-objective solution algorithms in the literature such as the genetic algorithm, the proposed multi-objective radial basis function algorithm is shown to be most effective. The algorithm is applied to a flexible robust toll pricing problem, where toll pricing is proposed as a strategy to manage network robustness over multiple regimes of link capacity uncertainty. A link degradation simulation model is proposed that uses multivariate Bernoulli random variables to simulate correlated link failures. The solution to a multi-objective mean-variance toll pricing problem is obtained for the Sioux Falls network under low and high probability seasons, showing that the flexibility to adapt the Pareto set of toll solutions to changes in regime – e.g. hurricane seasons, security threat levels, etc – can increase value in terms of an epsilon indicator.
With advances in emerging technologies, options for operating public transit services have broade... more With advances in emerging technologies, options for operating public transit services have broadened from conventional fixed-route service through semi-flexible service to on-demand microtransit. Nevertheless, guidelines for deciding between these services remain limited in the real implementation. An open-source simulation sandbox is developed that can compare state-of-the-practice methods for evaluating between the different types of public transit operations. For the case of the semi-flexible service, the Mobility Allowance Shuttle Transit (MAST) system is extended to include passenger deviations. A case study demonstrates the sandbox to evaluate and existing B63 bus route in Brooklyn, NY and compares its performance with the four other system designs spanning across the three service types for three different demand scenarios.
With the prevalence of MaaS systems, route choice models need to consider characteristics unique ... more With the prevalence of MaaS systems, route choice models need to consider characteristics unique to them. MaaS systems tend to involve service systems with fleets of vehicles; as a result, the available service capacity depends on the choices of other travelers in different parts of the system. We model this with a new concept of "congestible capacity"; that is, link capacities are a function of flow instead of link costs. This dependency is also non-separable; the capacity in one link can depend on flows from multiple links. An offline-online estimation method is introduced to capture the structural effects that flows have on capacities and the resulting impacts on route choice utilities. The method is first applied to obtain unique congestible capacity shadow prices in a multimodal network to verify the capability to capture congestion effects on capacities. The capacities are shown to vary and impact the utility of a route. The method is validated using real system data...
Microtransit and other flexible transit fleet services can reduce costs by incorporating transfer... more Microtransit and other flexible transit fleet services can reduce costs by incorporating transfers. However, transfers are costly to users if they have to get off a vehicle and wait at a stop for another pickup. A mixed integer linear programming model (MILP) is proposed to solve pickup and delivery problems with vehicle-synchronized en-route transfers (PDPSET). The transfer location is determined by the model and can be located at any candidate node in the network rather than a static facility defined in advance. The transfer operation is strictly synchronized between vehicles within a hard time window. A heuristic algorithm is proposed to solve the problem with an acceptable solution in a much shorter computation time than commercial software. Two sets of synthetic numerical experiments are tested: 20 instances based on a 5x5 grid network, and 42 different instances of varying network sizes up to 250x250 grids to test scalability. The results show that adding synchronized en-route...
An e-scooter trip model is estimated from four U.S. cities: Portland, Austin, Chicago and New Yor... more An e-scooter trip model is estimated from four U.S. cities: Portland, Austin, Chicago and New York City. A log-log regression model is estimated for e-scooter trips based on user age, population, land area, and the number of scooters. The model predicts 75K daily e-scooter trips in Manhattan for 2000 scooters, which translates to 77 million USD in annual revenue. We propose a novel nonlinear, multifactor model to break down the number of daily trips by the alternative modes of transportation that they would likely substitute. The model parameters reveal a relationship with direct trips of bike, walk, carpool, automobile and taxi as well as access/egress trips with public transit in Manhattan. Our model estimates that e-scooters would replace at most 32 carpool; 13 from access/egress trips is found to differ from that of other substituted trips.
Despite the dependency of electric vehicle (EV) fleets on charging station availability, charging... more Despite the dependency of electric vehicle (EV) fleets on charging station availability, charging infrastructure remains limited in many cities. Three contributions are made. First, we propose an EV-to-charging station user equilibrium (UE) assignment model with a M/D/C queue approximation as a nondifferentiable nonlinear program. Second, to address the non-differentiability of the queue delay function, we propose an original solution algorithm based on the derivative-free Method of Successive Averages. Computational tests with a toy network show that the model converges to a UE. A working code in Python is provided free on Github with detailed test cases. Third, the model is applied to the large-scale case study of NYC DCAS fleet and EV charging station configuration as of July 8, 2020, which includes unique, real data for 563 Level 2 chargers and 4 DCFCs owned by NYC and 1484 EVs owned by NYC fleets distributed over 512 TAZs. The arrival rates of the assignment model are calibrate...
In prior research, a statistically cheap method was developed to monitor transportation network p... more In prior research, a statistically cheap method was developed to monitor transportation network performance by using only a few groups of agents without having to forecast the population flows. The current study validates this "multi-agent inverse optimization" method using taxi GPS probe data from the city of Wuhan, China. Using a controlled 2062-link network environment and different GPS data processing algorithms, an online monitoring environment is simulated using the real data over a 4-hour period. Results show that using only samples from one OD pair, the multi-agent inverse optimization method can learn network parameters such that forecasted travel times have a 0.23 correlation with the observed travel times. By increasing to monitoring from just two OD pairs, the correlation improves further to 0.56.
As Mobility as a Service (MaaS) systems become increasingly popular, travel is changing from unim... more As Mobility as a Service (MaaS) systems become increasingly popular, travel is changing from unimodal trips to personalized services offered by a platform of mobility operators. Evaluation of MaaS platforms depends on modeling both user route decisions as well as operator service and pricing decisions. We adopt a new paradigm for traffic assignment in a MaaS network of multiple operators using the concept of stable matching to allocate costs and determine prices offered by operators corresponding to user route choices and operator service choices without resorting to nonconvex bilevel programming formulations. Unlike our prior work, the proposed model allows travelers to make multimodal, multi-operator trips, resulting in stable cost allocations between competing network operators to provide MaaS for users. An algorithm is proposed to efficiently generate stability conditions for the stable outcome model. Extensive computational experiments demonstrate the use of the model to handli...
The COVID-19 pandemic has affected travel behaviors and transportation system operations, and cit... more The COVID-19 pandemic has affected travel behaviors and transportation system operations, and cities are grappling with what policies can be effective for a phased reopening shaped by social distancing. A baseline model was previously developed and calibrated for pre-COVID conditions as MATSim-NYC. A new COVID model is calibrated that represents travel behavior during the COVID-19 pandemic by recalibrating the population agendas to include work-from-home and re-estimating the mode choice model for MATSim-NYC to fit observed traffic and transit ridership data. Assuming the change in behavior exhibits inertia during reopening, we analyze the increase in car traffic due to the phased reopen plan guided by the state government of New York. Four reopening phases and two reopening scenarios (with and without transit capacity restrictions) are analyzed. A Phase 4 reopening with 100% transit capacity may only see as much as 73% of pre-COVID ridership and an increase in the number of car tri...
Ubiquitous taxi trajectory data has made it possible to apply it to different types of travel ana... more Ubiquitous taxi trajectory data has made it possible to apply it to different types of travel analysis. Of interest is the need to allow someone to monitor travel momentum and associated congestion in any location in space in real time. However, despite an abundant literature in taxi data visualization and its applicability to travel analysis, no easy method exists. To measure taxi travel momentum at a location, current methods require filtering taxi trajectories that stop at a location at a particular time range, which is computationally expensive. We propose an alternative, computationally cheaper way based on pre-processing vector fields from the trajectories. Algorithms are formalized for generating vector kernel density to estimate a travel-model-free vector field-based representation of travel momentum in an urban space. The algorithms are shared online as an open source GIS 3D extension called VectorKD. Using 17 million daily taxi GPS points within Beijing over a four-day per...
We propose a generalized market equilibrium model using assignment game criteria for evaluating t... more We propose a generalized market equilibrium model using assignment game criteria for evaluating transportation systems that consist of both operators' and users' decisions. The model finds stable pricing, in terms of generalized costs, and matches between user populations in a network to set of routes with line capacities. The proposed model gives a set of stable outcomes instead of single point pricing that allows operators to design ticket pricing, routes/schedules that impact access/egress, shared policies that impact wait/transfer costs, etc., based on a desired mechanism or policy. The set of stable outcomes is proven to be convex from which assignment-dependent unique user-optimal and operator-optimal outcomes can be obtained. Different user groups can benefit from using this model in a prescriptive manner or within a sequential design process. We look at several different examples to test our model: small examples of fixed transit routes and a case study using a small...
Despite the ubiquity of transportation data, methods to infer the state parameters of a network e... more Despite the ubiquity of transportation data, methods to infer the state parameters of a network either ignore sensitivity of route decisions, require route enumeration for parameterizing descriptive models of route selection, or require complex bilevel models of route assignment behavior. These limitations prevent modelers from fully exploiting ubiquitous data in monitoring transportation networks. Inverse optimization methods that capture network route choice behavior can address this gap, but they are designed to take observations of the same model to learn the parameters of that model, which is statistically inefficient (e.g. requires estimating population route and link flows). New inverse optimization models and supporting algorithms are proposed to learn the parameters of heterogeneous travelers' route behavior to infer shared network state parameters (e.g. link capacity dual prices). The inferred values are consistent with observations of each agent's optimization beh...
This file consists of (1) NY UC beta version pre-beta test survey instrument for the City of New ... more This file consists of (1) NY UC beta version pre-beta test survey instrument for the City of New York, in English; (2) UC Release Candidate 1.0 field test Travel Log survey instrument, in English and Spanish; (3) UC Release Candidate 1.0 field text Exit Survey questionnaire survey instrument, in English and Spanish.
Viability of electric car-sharing operations depends on rebalancing algorithms. Earlier methods i... more Viability of electric car-sharing operations depends on rebalancing algorithms. Earlier methods in the literature suggest a trend toward non-myopic algorithms using queueing principles. We propose a new model formulation based on a static node-charge graph structure into a p-median relocation problem with minimum cost flow conservation and path-based charging station capacities. The model is NP-complete, so a heuristic is proposed that ensures feasible intermediate solutions that can be solved for an online system. Assessment of the algorithm in computational tests suggest optimality gaps of 8-20% among the tested instances of up to 1000 nodes while achieving 20x computational time savings needed for online application. The algorithm is validated in a case study of electric carshare in Brooklyn, New York, with demand data shared from BMW ReachNow operations in September 2017 (262 vehicle fleet, 231 pickups per day, 303 TAZs) and charging station location data (18 charging stations w...
Witnessing the rapid progress and accelerated commercialization made in recent years for the intr... more Witnessing the rapid progress and accelerated commercialization made in recent years for the introduction of air taxi services in near future across metropolitan cities, our research focuses on one of the most important consideration for such services, i.e., infrastructure planning (also known as skyports). We consider design of skyport locations for air taxis accessing airports, where we present the skyport location problem as a modified single-allocation p-hub median location problem integrating choice-constrained user mode choice behavior into the decision process. Our approach focuses on two alternative objectives i.e., maximizing air taxi ridership and maximizing air taxi revenue. The proposed models in the study incorporate trade-offs between trip length and trip cost based on mode choice behavior of travelers to determine optimal choices of skyports in an urban city. We examine the sensitivity of skyport locations based on two objectives, three air taxi pricing strategies, an...
2020 Forum on Integrated and Sustainable Transportation Systems (FISTS), 2020
This paper proposes a stochastic variant of the stable matching model from Rasulkhani and Chow [1... more This paper proposes a stochastic variant of the stable matching model from Rasulkhani and Chow [1] which allows microtransit operators to evaluate their operation policy and resource allocations. The proposed model takes into account the stochastic nature of users’ travel utility perception, resulting in a probabilistic stable operation cost allocation outcome to design ticket price and ridership forecasting. We applied the model for the operation policy evaluation of a microtransit service in Luxembourg and its border area. The methodology for the model parameters estimation and calibration is developed. The results provide useful insights for the operator and the government to improve the ridership of the service.
Despite the dependency of electric vehicle (EV) fleets on charging station availability, charging... more Despite the dependency of electric vehicle (EV) fleets on charging station availability, charging infrastructure remains limited in many cities. Three contributions are made. First, we propose an EV-to-charging station user equilibrium (UE) assignment model with a M/D/C queue approximation as a nondifferentiable nonlinear program. Second, to address the nondifferentiability of the queue delay function, we propose an original solution algorithm based on the derivative-free Method of Successive Averages. Computational tests with a toy network show that the model converges to a UE. A working code in Python is provided free on Github with detailed test cases. Third, the model is applied to the large-scale case study of New York City Department of Citywide Administrative Services (NYC DCAS) fleet and EV charging station configuration as of July 8, 2020, which includes unique, real data for 563 Level 2 chargers and 4 Direct Current Fast Chargers (DCFCs) and 1484 EVs distributed over 512 T...
With advances in emerging technologies, options for operating public transit services have broade... more With advances in emerging technologies, options for operating public transit services have broadened from conventional fixed-route service through semi-flexible service to on-demand microtransit. Nevertheless, guidelines for deciding between these services remain limited in the real implementation. An open-source simulation sandbox is developed that can compare state-ofthe-practice methods for evaluating between the different types of public transit operations. For the case of the semi-flexible service, the MAST system is extended to include passenger deviations. A case study demonstrates the sandbox to evaluate and existing B63 bus route in Brooklyn, NY and compares its performance with the four other system designs spanning across the three service types for three different demand scenarios.
Strategies, models, and algorithms facilitating such models are explored to provide transportatio... more Strategies, models, and algorithms facilitating such models are explored to provide transportation network managers and planners with more flexibility under uncertainty. Network design problems with non-stationary stochastic OD demand are formulated as real option investment problems and dynamic programming solution methodologies are used to obtain the value of flexibility to defer and re-design a network. The design premium is shown to reflect the opportunity cost of committing to a “preferred alternative” in transportation planning. Both network option and link option design problems are proposed with solution algorithms and tested on the classical Sioux Falls, SD network. Results indicate that allowing individual links to be deferred can have significant option value. A resource relocation model using non-stationary stochastic variables as chance constraints is proposed. The model is applied to air tanker relocation for initial attack of wildfires in California, and results show that the flexibility to switch locations with non-stationary stochastic variables providing 3-day or 7-day forecasts is more cost-effective than relocations without forecasting. Due to the computational costs of these more complex network models, a faster converging heuristic based on radial basis functions is evaluated for continuous network design problems for the Anaheim, CA network with a 31-dimensional decision variable. The algorithm is further modified and then proven to converge for multi-objective problems. Compared to other popular multi-objective solution algorithms in the literature such as the genetic algorithm, the proposed multi-objective radial basis function algorithm is shown to be most effective. The algorithm is applied to a flexible robust toll pricing problem, where toll pricing is proposed as a strategy to manage network robustness over multiple regimes of link capacity uncertainty. A link degradation simulation model is proposed that uses multivariate Bernoulli random variables to simulate correlated link failures. The solution to a multi-objective mean-variance toll pricing problem is obtained for the Sioux Falls network under low and high probability seasons, showing that the flexibility to adapt the Pareto set of toll solutions to changes in regime – e.g. hurricane seasons, security threat levels, etc – can increase value in terms of an epsilon indicator.
With advances in emerging technologies, options for operating public transit services have broade... more With advances in emerging technologies, options for operating public transit services have broadened from conventional fixed-route service through semi-flexible service to on-demand microtransit. Nevertheless, guidelines for deciding between these services remain limited in the real implementation. An open-source simulation sandbox is developed that can compare state-of-the-practice methods for evaluating between the different types of public transit operations. For the case of the semi-flexible service, the Mobility Allowance Shuttle Transit (MAST) system is extended to include passenger deviations. A case study demonstrates the sandbox to evaluate and existing B63 bus route in Brooklyn, NY and compares its performance with the four other system designs spanning across the three service types for three different demand scenarios.
With the prevalence of MaaS systems, route choice models need to consider characteristics unique ... more With the prevalence of MaaS systems, route choice models need to consider characteristics unique to them. MaaS systems tend to involve service systems with fleets of vehicles; as a result, the available service capacity depends on the choices of other travelers in different parts of the system. We model this with a new concept of "congestible capacity"; that is, link capacities are a function of flow instead of link costs. This dependency is also non-separable; the capacity in one link can depend on flows from multiple links. An offline-online estimation method is introduced to capture the structural effects that flows have on capacities and the resulting impacts on route choice utilities. The method is first applied to obtain unique congestible capacity shadow prices in a multimodal network to verify the capability to capture congestion effects on capacities. The capacities are shown to vary and impact the utility of a route. The method is validated using real system data...
Microtransit and other flexible transit fleet services can reduce costs by incorporating transfer... more Microtransit and other flexible transit fleet services can reduce costs by incorporating transfers. However, transfers are costly to users if they have to get off a vehicle and wait at a stop for another pickup. A mixed integer linear programming model (MILP) is proposed to solve pickup and delivery problems with vehicle-synchronized en-route transfers (PDPSET). The transfer location is determined by the model and can be located at any candidate node in the network rather than a static facility defined in advance. The transfer operation is strictly synchronized between vehicles within a hard time window. A heuristic algorithm is proposed to solve the problem with an acceptable solution in a much shorter computation time than commercial software. Two sets of synthetic numerical experiments are tested: 20 instances based on a 5x5 grid network, and 42 different instances of varying network sizes up to 250x250 grids to test scalability. The results show that adding synchronized en-route...
An e-scooter trip model is estimated from four U.S. cities: Portland, Austin, Chicago and New Yor... more An e-scooter trip model is estimated from four U.S. cities: Portland, Austin, Chicago and New York City. A log-log regression model is estimated for e-scooter trips based on user age, population, land area, and the number of scooters. The model predicts 75K daily e-scooter trips in Manhattan for 2000 scooters, which translates to 77 million USD in annual revenue. We propose a novel nonlinear, multifactor model to break down the number of daily trips by the alternative modes of transportation that they would likely substitute. The model parameters reveal a relationship with direct trips of bike, walk, carpool, automobile and taxi as well as access/egress trips with public transit in Manhattan. Our model estimates that e-scooters would replace at most 32 carpool; 13 from access/egress trips is found to differ from that of other substituted trips.
Despite the dependency of electric vehicle (EV) fleets on charging station availability, charging... more Despite the dependency of electric vehicle (EV) fleets on charging station availability, charging infrastructure remains limited in many cities. Three contributions are made. First, we propose an EV-to-charging station user equilibrium (UE) assignment model with a M/D/C queue approximation as a nondifferentiable nonlinear program. Second, to address the non-differentiability of the queue delay function, we propose an original solution algorithm based on the derivative-free Method of Successive Averages. Computational tests with a toy network show that the model converges to a UE. A working code in Python is provided free on Github with detailed test cases. Third, the model is applied to the large-scale case study of NYC DCAS fleet and EV charging station configuration as of July 8, 2020, which includes unique, real data for 563 Level 2 chargers and 4 DCFCs owned by NYC and 1484 EVs owned by NYC fleets distributed over 512 TAZs. The arrival rates of the assignment model are calibrate...
In prior research, a statistically cheap method was developed to monitor transportation network p... more In prior research, a statistically cheap method was developed to monitor transportation network performance by using only a few groups of agents without having to forecast the population flows. The current study validates this "multi-agent inverse optimization" method using taxi GPS probe data from the city of Wuhan, China. Using a controlled 2062-link network environment and different GPS data processing algorithms, an online monitoring environment is simulated using the real data over a 4-hour period. Results show that using only samples from one OD pair, the multi-agent inverse optimization method can learn network parameters such that forecasted travel times have a 0.23 correlation with the observed travel times. By increasing to monitoring from just two OD pairs, the correlation improves further to 0.56.
As Mobility as a Service (MaaS) systems become increasingly popular, travel is changing from unim... more As Mobility as a Service (MaaS) systems become increasingly popular, travel is changing from unimodal trips to personalized services offered by a platform of mobility operators. Evaluation of MaaS platforms depends on modeling both user route decisions as well as operator service and pricing decisions. We adopt a new paradigm for traffic assignment in a MaaS network of multiple operators using the concept of stable matching to allocate costs and determine prices offered by operators corresponding to user route choices and operator service choices without resorting to nonconvex bilevel programming formulations. Unlike our prior work, the proposed model allows travelers to make multimodal, multi-operator trips, resulting in stable cost allocations between competing network operators to provide MaaS for users. An algorithm is proposed to efficiently generate stability conditions for the stable outcome model. Extensive computational experiments demonstrate the use of the model to handli...
The COVID-19 pandemic has affected travel behaviors and transportation system operations, and cit... more The COVID-19 pandemic has affected travel behaviors and transportation system operations, and cities are grappling with what policies can be effective for a phased reopening shaped by social distancing. A baseline model was previously developed and calibrated for pre-COVID conditions as MATSim-NYC. A new COVID model is calibrated that represents travel behavior during the COVID-19 pandemic by recalibrating the population agendas to include work-from-home and re-estimating the mode choice model for MATSim-NYC to fit observed traffic and transit ridership data. Assuming the change in behavior exhibits inertia during reopening, we analyze the increase in car traffic due to the phased reopen plan guided by the state government of New York. Four reopening phases and two reopening scenarios (with and without transit capacity restrictions) are analyzed. A Phase 4 reopening with 100% transit capacity may only see as much as 73% of pre-COVID ridership and an increase in the number of car tri...
Ubiquitous taxi trajectory data has made it possible to apply it to different types of travel ana... more Ubiquitous taxi trajectory data has made it possible to apply it to different types of travel analysis. Of interest is the need to allow someone to monitor travel momentum and associated congestion in any location in space in real time. However, despite an abundant literature in taxi data visualization and its applicability to travel analysis, no easy method exists. To measure taxi travel momentum at a location, current methods require filtering taxi trajectories that stop at a location at a particular time range, which is computationally expensive. We propose an alternative, computationally cheaper way based on pre-processing vector fields from the trajectories. Algorithms are formalized for generating vector kernel density to estimate a travel-model-free vector field-based representation of travel momentum in an urban space. The algorithms are shared online as an open source GIS 3D extension called VectorKD. Using 17 million daily taxi GPS points within Beijing over a four-day per...
We propose a generalized market equilibrium model using assignment game criteria for evaluating t... more We propose a generalized market equilibrium model using assignment game criteria for evaluating transportation systems that consist of both operators' and users' decisions. The model finds stable pricing, in terms of generalized costs, and matches between user populations in a network to set of routes with line capacities. The proposed model gives a set of stable outcomes instead of single point pricing that allows operators to design ticket pricing, routes/schedules that impact access/egress, shared policies that impact wait/transfer costs, etc., based on a desired mechanism or policy. The set of stable outcomes is proven to be convex from which assignment-dependent unique user-optimal and operator-optimal outcomes can be obtained. Different user groups can benefit from using this model in a prescriptive manner or within a sequential design process. We look at several different examples to test our model: small examples of fixed transit routes and a case study using a small...
Despite the ubiquity of transportation data, methods to infer the state parameters of a network e... more Despite the ubiquity of transportation data, methods to infer the state parameters of a network either ignore sensitivity of route decisions, require route enumeration for parameterizing descriptive models of route selection, or require complex bilevel models of route assignment behavior. These limitations prevent modelers from fully exploiting ubiquitous data in monitoring transportation networks. Inverse optimization methods that capture network route choice behavior can address this gap, but they are designed to take observations of the same model to learn the parameters of that model, which is statistically inefficient (e.g. requires estimating population route and link flows). New inverse optimization models and supporting algorithms are proposed to learn the parameters of heterogeneous travelers' route behavior to infer shared network state parameters (e.g. link capacity dual prices). The inferred values are consistent with observations of each agent's optimization beh...
This file consists of (1) NY UC beta version pre-beta test survey instrument for the City of New ... more This file consists of (1) NY UC beta version pre-beta test survey instrument for the City of New York, in English; (2) UC Release Candidate 1.0 field test Travel Log survey instrument, in English and Spanish; (3) UC Release Candidate 1.0 field text Exit Survey questionnaire survey instrument, in English and Spanish.
Viability of electric car-sharing operations depends on rebalancing algorithms. Earlier methods i... more Viability of electric car-sharing operations depends on rebalancing algorithms. Earlier methods in the literature suggest a trend toward non-myopic algorithms using queueing principles. We propose a new model formulation based on a static node-charge graph structure into a p-median relocation problem with minimum cost flow conservation and path-based charging station capacities. The model is NP-complete, so a heuristic is proposed that ensures feasible intermediate solutions that can be solved for an online system. Assessment of the algorithm in computational tests suggest optimality gaps of 8-20% among the tested instances of up to 1000 nodes while achieving 20x computational time savings needed for online application. The algorithm is validated in a case study of electric carshare in Brooklyn, New York, with demand data shared from BMW ReachNow operations in September 2017 (262 vehicle fleet, 231 pickups per day, 303 TAZs) and charging station location data (18 charging stations w...
Witnessing the rapid progress and accelerated commercialization made in recent years for the intr... more Witnessing the rapid progress and accelerated commercialization made in recent years for the introduction of air taxi services in near future across metropolitan cities, our research focuses on one of the most important consideration for such services, i.e., infrastructure planning (also known as skyports). We consider design of skyport locations for air taxis accessing airports, where we present the skyport location problem as a modified single-allocation p-hub median location problem integrating choice-constrained user mode choice behavior into the decision process. Our approach focuses on two alternative objectives i.e., maximizing air taxi ridership and maximizing air taxi revenue. The proposed models in the study incorporate trade-offs between trip length and trip cost based on mode choice behavior of travelers to determine optimal choices of skyports in an urban city. We examine the sensitivity of skyport locations based on two objectives, three air taxi pricing strategies, an...
2020 Forum on Integrated and Sustainable Transportation Systems (FISTS), 2020
This paper proposes a stochastic variant of the stable matching model from Rasulkhani and Chow [1... more This paper proposes a stochastic variant of the stable matching model from Rasulkhani and Chow [1] which allows microtransit operators to evaluate their operation policy and resource allocations. The proposed model takes into account the stochastic nature of users’ travel utility perception, resulting in a probabilistic stable operation cost allocation outcome to design ticket price and ridership forecasting. We applied the model for the operation policy evaluation of a microtransit service in Luxembourg and its border area. The methodology for the model parameters estimation and calibration is developed. The results provide useful insights for the operator and the government to improve the ridership of the service.
Despite the dependency of electric vehicle (EV) fleets on charging station availability, charging... more Despite the dependency of electric vehicle (EV) fleets on charging station availability, charging infrastructure remains limited in many cities. Three contributions are made. First, we propose an EV-to-charging station user equilibrium (UE) assignment model with a M/D/C queue approximation as a nondifferentiable nonlinear program. Second, to address the nondifferentiability of the queue delay function, we propose an original solution algorithm based on the derivative-free Method of Successive Averages. Computational tests with a toy network show that the model converges to a UE. A working code in Python is provided free on Github with detailed test cases. Third, the model is applied to the large-scale case study of New York City Department of Citywide Administrative Services (NYC DCAS) fleet and EV charging station configuration as of July 8, 2020, which includes unique, real data for 563 Level 2 chargers and 4 Direct Current Fast Chargers (DCFCs) and 1484 EVs distributed over 512 T...
With advances in emerging technologies, options for operating public transit services have broade... more With advances in emerging technologies, options for operating public transit services have broadened from conventional fixed-route service through semi-flexible service to on-demand microtransit. Nevertheless, guidelines for deciding between these services remain limited in the real implementation. An open-source simulation sandbox is developed that can compare state-ofthe-practice methods for evaluating between the different types of public transit operations. For the case of the semi-flexible service, the MAST system is extended to include passenger deviations. A case study demonstrates the sandbox to evaluate and existing B63 bus route in Brooklyn, NY and compares its performance with the four other system designs spanning across the three service types for three different demand scenarios.
2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 2017
Despite the increasing relevance of private transport operators as Mobility-as-a-Service in the s... more Despite the increasing relevance of private transport operators as Mobility-as-a-Service in the success of smart cities, desire for privacy in data sharing limits collaborations with public agencies. We propose an original model that circumvents this limitation, by designing a diffusion of the data — in this case, service tour data — such that passenger travel times remain reliable to the recipient agency. The Tour Sharing Privacy Design Problem is formulated as a nonlinear programming problem that maximizes entropy. We investigate properties of the model and iterative tour generation algorithms, and conduct a series of numerical experiments on an instance that has 90 feasible tours. The experimental results show that a k-best shortest tour approach of generating tours iteratively initially increases the gap to a lower bound before decreasing toward a final constraint gap. The model is shown to recognize the trade-offs between more reliability in data and more anonymity. Comparisons...
The MATALB functions in this document are used in the article:
Chow, J. Y. J., 2016. Dynamic UAV-... more The MATALB functions in this document are used in the article: Chow, J. Y. J., 2016. Dynamic UAV-based traffic monitoring under uncertainty as a stochastic arc-inventory routing policy. International Journal of Transportation Science & Technology, in press.
SDAIRP corresponds to Algorithm 1 in the paper in Section 4.2. SARP uses MATLAB’s commercial IP solver (intlinprog) to solve a selective VRP as formulated in Eq (5) in the paper.
The code is for the inverse optimization model in this paper: Chow, J.Y.J., Recker, W.W., 2012. I... more The code is for the inverse optimization model in this paper: Chow, J.Y.J., Recker, W.W., 2012. Inverse optimization with endogenous arrival time constraints to calibrate the household activity pattern problem, Transportation Research Part B 46(3), 463-479. Note: unfortunately, a bug was found in the code after publishing the paper, it has since been fixed. Note that while this code is used to calibrate HAPP, it can also be modified to calibrate any multiobjective VRP where the objective parameters and/or arrival time constraints are not known with absolute certainty.
Guest Editors:
Yongxi (Eric) Huang, Clemson University, USA, yxhuang@clemson.edu
Michael Kuby, ... more Guest Editors:
Yongxi (Eric) Huang, Clemson University, USA, yxhuang@clemson.edu Michael Kuby, Arizona State University, USA, mikekuby@asu.edu Joseph Y.J. Chow, Ryerson University, Canada, joseph.chow@ryerson.ca
Introduction
The increasing pace of worldwide modernization and urbanization now requires the creation of more sustainable approaches to mitigate the effects of climate change and to manage large urban population centers. The transportation sector is facing a grand challenge regarding the evolution of new renewable energy sources to reduce the dependence on oil with a concurrent environmental stewardship. Recent technological advances in alternative fuel vehicles (AFVs) (e.g., electric, natural gas, biofuel, and hydrogen) lend evidence to that continuing evolution.
However, before AFVs can be widely adopted in the marketplace, several deficiencies in these systems must first be resolved. For example, more alternative fueling stations (AFSs) must be built to alleviate range anxiety. New routing and scheduling strategies must be designed for service fleets and public transit systems that employ AFV technologies. Complex (often dynamic and nonlinear) interactions between users, technologies, and the underlying economy need to be better understood. Technologies and systems solutions are thereby needed in order to better understand the interactions between infrastructure, vehicles, and the users.
Scope of the Special Issue
This special issue will be focused on new problem formulations, models, and more effective exact or approximate solution approaches. Potential topics could come from different aspects that include but are not limited to the following:
• AFS near-and-long term planning on network
• Alternative fuel transit system planning and operations
• Smart AFV routing strategies
• Interactions between users, technologies, and economy
• Integrated models to evaluate broad impacts of AFV technologies, e.g., transportation-energy-land use interactions
• Interaction of public and private sectors in driving the technologies to marketplace
• New business/operational models enabled by AFVs, e.g., vehicle-to-grid operations
Submission Method
All submissions will go through the journal’s standard peer-review process. For guidelines to prepare your manuscript and for manuscript submission, please visit http://ees.elsevier.com/trc. When submitting your manuscript, please choose “SI: AFV transport systems” for “Article Type”. This is to ensure that your submission will be considered for this special issue instead of being handled as a regular paper. All submissions will be subject to the journal’s standard peer review process. Criteria for acceptance include originality, contribution, and scientific merit.
Important Dates
Submission website opens: January 5, 2015
Submission deadline of full paper online: April 5, 2015
Feedback from first-round reviews: July 15, 2015
Feedback from second-round reviews (if indicated): October 15, 2015
Final manuscripts due: December 15, 2015
Planned publication: 2016
This script employs a Lagrangian relaxation approach to break out the problem into a master probl... more This script employs a Lagrangian relaxation approach to break out the problem into a master problem and subproblems that solve each day independently using updated dual prices (both scripts provided in document). Full paper at dx.doi.org/10.1080/23249935.2014.958120
"This is a script used to simulate correlated link failures in a network. A multivariate Bernoull... more "This is a script used to simulate correlated link failures in a network. A multivariate Bernoulli distribution is assumed, requiring only the mean marginal Bernoulli failure rates and the correlation matrix between each link as input. Details of the simulation model and its evaluation are available in the following:
Chow, J.Y.J., Regan, A.C., 2013. A surrogate-based multiobjective metaheuristic and network degradation simulation model for robust toll pricing. Optimization and Engineering, accepted for publication.
The model was used to generate failure scenarios for evaluating a robust toll pricing policy. The original method was devised in:
Curtis, W., Zikan, K., Sowizral, H., 2006. Random Sampling for Multivariate Bernoulli Variables. United States Patent, No. US 7,006,954 B1, Feb 28, 2006."
"This is a script for the MO-RBF algorithm, which is a fast RBF-based heuristic for solving k-obj... more "This is a script for the MO-RBF algorithm, which is a fast RBF-based heuristic for solving k-objective optimization problems. Details of the algorithm can be found in:
Chow, J.Y.J., Regan, A.C., 2013. A surrogate-based multiobjective metaheuristic and network degradation simulation model for robust toll pricing. Optimization and Engineering, accepted for publication.
This script was used to test the 5 test functions discussed in sections 3.3-3.4 in the paper above, and modified to obtain the Pareto set for a robust toll pricing problem (presented in a separate teaching document)."
This is a script I used to solve the Generalized Selective Household Activity Routing Problem usi... more This is a script I used to solve the Generalized Selective Household Activity Routing Problem using GA (benchmark algorithm from which I tested my reoptimization algorithm). G-SHARP is an extension of Recker's (1995) HAPP model, to include different activity types, candidate destinations by type, and destination choice along with the original routing and scheduling for a household. It allows for both compulsory and non-compulsory activity types. For more information, see my paper on: "Activity-based travel scenario analysis with routing problem reoptimization". Disclaimer: this is meant to illustrate one implementation of the algorithm, please do not assume it is ready to be run straight out of the box.
This is code that I used to test the concept of reoptimization for analyzing travel scenarios. Th... more This is code that I used to test the concept of reoptimization for analyzing travel scenarios. The idea is that the household travel survey data that we collect is "optimal" if we calibrate parameters of itinerary assignment to them, so for travel scenario analysis we don't need to solve HAPP (or in this case, SHARP) from scratch. Instead, we can just reoptimize using the single survey trajectories as the prior optimal solution. The problem is still NP-hard, but heuristics tend to perform better than heuristics solving from scratch. The paper is published as:
"Chow, J.Y.J., 2012. Activity-based travel scenario analysis with routing problem reoptimization. Computer-Aided Civil and Infrastructure Engineering, Special Issue on Computational Methods for Advanced Transportation Planning, eds. S.T. Waller and C.R. Bhat, accepted for publication. "
I created this spreadsheet in 2007 to calculate the winning percentages based on the number of at... more I created this spreadsheet in 2007 to calculate the winning percentages based on the number of attackers and defenders in a battle in the RISK board game.
This is code that I wrote in 2011 for jointly estimating the parameters and states of a chaotic s... more This is code that I wrote in 2011 for jointly estimating the parameters and states of a chaotic series. The algorithm is from:
Nakamura, T., Hirata, Y., Judd, K., Kilminster, D., 2007. Improved parameter estimation from noisy time series for nonlinear dynamical systems. International Journal of Bifurcation and Chaos 17(5), 1741-1752.
I modified their method to have a smoother descent using Method of Successive Averages. The purpose was to apply this method to estimate chaotic arrivals in a chaos-driven queue, which is both deterministic and unpredictable, allowing one to analyze transient properties and propagate complex queue patterns over a network. This work is in:
Chow, J.Y.J., 2013. On observable chaotic maps for queueing analysis. Transportation Research Record, accepted for publication.
"This is Matlab code that I developed in 2009 for solving a server relocation problem under stoch... more "This is Matlab code that I developed in 2009 for solving a server relocation problem under stochastic demand, using a rolling horizon. The code can be easily modified to handle relocation without rolling horizon, or just a p-median problem with or without rolling horizon. The version I have here includes co-location of servers. It requires calling an IP solver, and the code uses discretized mean-reverting processes for demand at each node, which can be modified to other cases. It was applied to the following paper:
Chow, J.Y.J., Regan, A.C., 2011. Resource location and relocation models with rolling horizon forecasting for wildland fire planning. INFOR 49(1), 31-43."
Developed by Ankoor Bhagat and Sarah Hernandez based on the method described in "Multicriteria su... more Developed by Ankoor Bhagat and Sarah Hernandez based on the method described in "Multicriteria sustainability assessment in transport planning for recreational travel" and extended from Jeon et al. (2010). This is for educational purposes only, and it is the responsibility of the user to check for mistakes.
Talk given at International Workshop on Activity-Travel Behavior Analysis and Multi-state Superne... more Talk given at International Workshop on Activity-Travel Behavior Analysis and Multi-state Supernetwork Modeling hosted by HKSTS in 2014. Work in collaboration with Iris You and Stephen Ritchie, published in 2016 under doi 10.1080/23249935.2016.1189723 in Transportmetrica A special issue.
Talk given at KAIST on Nov 22nd, 2015. Development of urban transportation networks (e.g. public ... more Talk given at KAIST on Nov 22nd, 2015. Development of urban transportation networks (e.g. public transit, roadways, charging infrastructure) is often very costly and prone to uncertainty in demand for its usage, as noted in numerous studies by Bent Flyvbjerg and others. The risk is compounded in scenarios like transportation networks, where adding extra capacity can actually worsen traffic conditions due to network effects, as demonstrated by the classic Braess’ Paradox. Dynamic decision-making can be introduced to accommodate the uncertainty, but the modeling of adaptive network design as a dynamic decision process is computationally challenging due to the curse of dimensionality. An overview is given for how to design and time network improvements over multiple periods under uncertainty using approximate dynamic programming methodologies taken from the option pricing literature. A discussion of open research questions and application to charging infrastructure design is presented.
A talk given at the Western Canada Connected Vehicle Workshop hosted at University of Alberta, or... more A talk given at the Western Canada Connected Vehicle Workshop hosted at University of Alberta, organized by Dr. Tony Qiu.
In the spirit of collaboration, I present a research problem in this seminar that is related to m... more In the spirit of collaboration, I present a research problem in this seminar that is related to multimodal route choice behavior. As we witness more information and communications technologies driving advances in mobility systems for people—services like dynamic dial a ride, Uber, Zipcar, SF Park, Google self-driving cars, etc.—the options available to public agencies continue to grow. However, many of the decision support tools available to agencies are either designed for traffic networks or unimodal fixed route transit services. Some of the behavioral methods available for these types of systems, e.g. random utility models, may not work as well in addressing particular issues in multimodal systems such as spatial-temporal scenarios or infrastructure operations. In recent years, advances have been made in inferring travel objectives and spatial-temporal constraints of individuals based on real time data. I provide an overview of one of these methods from an agent-based context and demonstrate its potential applicability to this problem through several examples related to urban route design, first/last mile transit problem, and activity-based travel scheduling. I conclude with a research challenge to design an “ensemble methodology” based on combining multiple models—random utility modeling in conjunction with “inverse transportation problems”—to tackle a wider range of policies and scenarios.
Due to the complex nature of the urban setting as a network of infrastructure operated as a publi... more Due to the complex nature of the urban setting as a network of infrastructure operated as a public good, optimization models are often employed in transportation operations, planning, and logistics. Parameters of these models are often difficult to obtain in a straightforward manner: e.g. public agencies might not have data available from private sector to determine how they operate; travelers exhibit a wide variation in taste, preference, and circumstance in deciding their travel patterns; parameters of networks may only be observed dynamically without an easy way to derive their ergodic properties. With the prevalence of Big Data, it is now possible to use the observed outputs to reverse engineer (even autonomously) the parameters of the models from which only partial input information was available before. In the context of optimization models, this is called “inverse optimization”.
In this seminar, the concept is introduced along with its applicability to mathematical programming problems: linear programming, integer programming, and nonlinear programming. Each can be applied to specific transportation problems: inverse shortest path problem when travel costs are uncertain but routes are observed; inverse vehicle routing problems to infer activity-based travel behavior from a population of observed travel patterns; inverse traffic assignment problems that work well for freight modeling with incomplete information about transshipment facility parameters. A fixed point prior for a sample population is introduced for regularization. Related ongoing research in activity-based modeling, network design, and urban logistics and how they will enhance the opportunities available from urban Big Data will be discussed.
An overview of current ongoing research trends is initiated (with further follow up dialog in the... more An overview of current ongoing research trends is initiated (with further follow up dialog in the panel and post-network sessions expected) on the role and perspective of transportation systems planning for infrastructure to facilitate alternative fuel technologies including hydrogen. Research from around the world has focused on infrastructure planning, better understanding user behavior, and policy evaluation. A recent study conducted by the presenter in collaboration with researchers in the United States and South Korea is discussed.
In travel demand forecasting, activity-based models are powerful tools that can capture behaviour... more In travel demand forecasting, activity-based models are powerful tools that can capture behavioural aspects of travel choices. However, they generally lack a cohesive spatial-temporal constrained utility maximizing framework. Recker proposed a mixed integer linear programming (MILP) problem as a potential solution, based on the pickup and delivery problem with time windows. Although this normative approach was developed to address the issue of capturing spatial-temporal constraints in a utility-maximization framework for activity-based modeling, one key issue lingered. There is a need to estimate the parameters for each household given multiple objectives such that observed arrival times and sequences can both be replicated. An approach was recently proposed and tested using an inverse optimization method. It allows household scheduling to be assigned to individual households in a disaggregate manner, analogous to the traffic assignment problem for aggregate travel forecasting. Potential to integrate the schedule assignment model with existing activity based models will be discussed, as well as ongoing research in activity integration and network design – in particular, key directions in public transit network design and cyber-physical transit systems proposed for my Canada Research Chair program will be highlighted.
Urban logistics systems consist of networked infrastructure or vehicle fleets serving the flow of... more Urban logistics systems consist of networked infrastructure or vehicle fleets serving the flow of travelers, goods, energy, or information in an urban context, e.g. public transit, freight and energy distribution, or road networks. A smart system is one that can adapt to changes in the environment and is aware of the behavioural needs of its users. This seminar addresses a question asked by policymakers in an age of ubiquitous data, urbanization, and need for sustainability: once we have an “instrumented city” setting where continuous streams of data are available, how can we make the most use of it? We touch upon two solutions and new questions that they bring.
The first is to introduce flexibility in the design of transportation networks. The basic network design problem is introduced along with its limitations. The static problem is modified to a stochastic dynamic programming formulation using approximate dynamic programming solution methods from real option theory to overcome the “curse of dimensionality”. This approach allows system designs to be viewed as options over time instead of as commitments, which explicitly incorporate the value of flexibility under uncertainty.
The second is incorporating user awareness. Systems designs often neglect the behavioral responses of users. We introduce the concept of an “activity-based network design problem”, where the design of a network is not based on pre-defined OD demand, but instead outputs the demand as a consequence of the network design and the users’ inherent preferences for activities.
Ongoing research and challenges are highlighted. Foremost among these challenges are the need to design and evaluate transport service systems at an urban level (e.g. if bike-sharing were incorporated by the City of Toronto as an extended service of the TTC, how would people’s travel patterns adjust and adopt the service? How would parking prices be affected?); the need to visualize these changes in a portfolio framework (as interacting assets that change value over time visualized in a GIS platform); and the need to validate service design strategies and operational business models using mobile device-based test beds.
Non-myopic dial-a-ride problem and other related dynamic vehicle routing problems often ignore th... more Non-myopic dial-a-ride problem and other related dynamic vehicle routing problems often ignore the need for non-myopic pricing under the assumption of elastic demand, which leads to an overestimation of the benefits in level of service and resulting inefficiencies. To correct this problem, a new dynamic dial-a-ride policy is introduced, one that features non-myopic pricing based on optimal tolling of queues to fit with the multi-server queueing approximation method proposed by Hyytiä et al (2012) for large-scale systems. By including social optimal pricing, the social welfare of the resulting system outperforms the marginal pricing assumed for previous approaches over a range of test instances. In the examples tested, improvements in social welfare of the non-myopic pricing over the myopic pricing were in the 20% - 31% range. For a given demand function, we can derive the optimal fleet size to maximize social welfare. Sensitivity tests to the optimal price confirm that it leads to an optimal social welfare while the marginal pricing policy does not. A comparison of single passenger taxis to shared-taxis shows that system cost may reduce at the expense of decreased social welfare, which agrees with the results of Jung et al. (2013).
Availability of real time “Big data” in recent years has driven an increasing interest in dynamic... more Availability of real time “Big data” in recent years has driven an increasing interest in dynamic/real-time/online/sequential network design models. Despite a growing number of studies in stochastic dynamic network optimization, the field remains less well defined and unified than other areas of network optimization. Due to the need for approximation methods like approximate dynamic programming, one of the most significant problems yet to be solved is the lack of adequate benchmarks. Common benchmark policies are inadequate; the value of the perfect information policy does not include random effects while the static and myopic policies are not sensitive to value of anticipation due to network structure. We propose a new class of network-sensitive reference policies using extreme value distributions to estimate theoretically consistent real option values based on sampled sequences. The reference policy is shown to fit known sequence policies well (particularly Weibull), and has sampling consistency for more than 200 samples. It is applied to sequential versions of the discrete network design and timing problem on the Sioux Falls network, the facility location and timing problem on the Simchi-Levi and Berman (1988) network, and Hyytiä et al.’s (2012) dial-a-ride problem.
As urbanization increases and new business models for transportation and mobility arise, the desi... more As urbanization increases and new business models for transportation and mobility arise, the design of transportation networks should no longer be done in a vacuum. Design interactions between multiple networks have largely been analyzed either as non-cooperative games with non-unique Nash equilibria, even if assumptions needed for such games are not satisfied, or using knowledge-based or agent-based methods that cannot explicitly quantify network sensitivities. A new framework is proposed to model network design in the presence of coexisting networks using multiobjective optimization in a novel manner to identify symbiotic relationships. The framework does not require strict assumptions about availability of information or timing of decisions, and it can be used to examine network sensitivities that knowledge-based methods cannot. A bundled discount pricing problem and subsidy problem are derived from the symbiotic relationships. The framework is applied to formulate a symbiotic bike-sharing network design problem in the presence of a coexisting transit system as a departure-time-elastic multicommodity flow problem. A
small network example demonstrates the potential dependency between transit systems and bike-sharing systems for the first time, and the existence of an optimal discount value for considering bundled fares. A larger bike-sharing network, BIXI, is examined in the presence of the Toronto Transit Commission (TTC) in downtown Toronto to address the question of subsidy. It is found that BIXI is operating in a relatively transit-friendly state, and subsidy by TTC to maintain a status quo in Toronto may be worth considering if the cost of subsidy is less than a conservative average reduction achieved of 2.43 units of
transit-only user cost for every 1 unit increase of bike-sharing cost.
A number of qualities make regional freight forecasting quite different from urban passenger dema... more A number of qualities make regional freight forecasting quite different from urban passenger demand modeling. One of these qualities is the need to capture sensitivity of the forecast model to freight facility investments. In order to do this, a network approach is inserted into the conventional four step model between mode choice and network assignment. The idea is to use network flow constraints to assign commodity flows to vehicle flows while maintaining flow conservation of the vehicles. By doing so, we can model empty truck hauls at the link level, shipper choice of freight facility to load commodities that includes congestion (e.g. Which airports should be used to ship commodities by air when considering both distance to the airport and delays in loading at the airport?), and observe vehicle tour patterns at the link level. A systematic approach to estimate those capacities will be discussed, and examples from a simple network as well as a validation effort using the California air freight setting will be presented.
"A queueing model based on chaotic mapping offers a number of distinct advantages over both stoch... more "A queueing model based on chaotic mapping offers a number of distinct advantages over both stochastic and static deterministic models. Depending on the type of chaotic map used, such a queue
can capture transient behavior, intermittency, steady state behavior, and complex distributions in arrival rates. These characteristics are especially desirable in many queueing applications in transportation. Earlier studies resulted in chaotic queueing models that cannot be estimated using observed arrivals. An alternative queueing model is presented along with methods to specify the model, interpret its results, and estimate its parameters. The proposed queueing model uses chaotic maps of inter-arrival times to generate arrivals so that parameters can be calibrated with observable data. A sample queue based on the ergodic logistic map is presented. To calibrate the mapping based on observed data, a joint parameter and state estimation algorithm is presented using the method of successive averages. An illustration is made with two connected queues to show how a purely deterministic queueing network can still result in a joint invariant distribution. The results offer a positive view of this method and its applicability to queueing problems, particularly in the field of transportation and dynamic network loading."
Uploads
Chow, J. Y. J., 2016. Dynamic UAV-based traffic monitoring under uncertainty as a stochastic arc-inventory routing policy. International Journal of Transportation Science & Technology, in press.
SDAIRP corresponds to Algorithm 1 in the paper in Section 4.2. SARP uses MATLAB’s commercial IP solver (intlinprog) to solve a selective VRP as formulated in Eq (5) in the paper.
Yongxi (Eric) Huang, Clemson University, USA, yxhuang@clemson.edu
Michael Kuby, Arizona State University, USA, mikekuby@asu.edu
Joseph Y.J. Chow, Ryerson University, Canada, joseph.chow@ryerson.ca
Introduction
The increasing pace of worldwide modernization and urbanization now requires the creation of more sustainable approaches to mitigate the effects of climate change and to manage large urban population centers. The transportation sector is facing a grand challenge regarding the evolution of new renewable energy sources to reduce the dependence on oil with a concurrent environmental stewardship. Recent technological advances in alternative fuel vehicles (AFVs) (e.g., electric, natural gas, biofuel, and hydrogen) lend evidence to that continuing evolution.
However, before AFVs can be widely adopted in the marketplace, several deficiencies in these systems must first be resolved. For example, more alternative fueling stations (AFSs) must be built to alleviate range anxiety. New routing and scheduling strategies must be designed for service fleets and public transit systems that employ AFV technologies. Complex (often dynamic and nonlinear) interactions between users, technologies, and the underlying economy need to be better understood. Technologies and systems solutions are thereby needed in order to better understand the interactions between infrastructure, vehicles, and the users.
Scope of the Special Issue
This special issue will be focused on new problem formulations, models, and more effective exact or approximate solution approaches. Potential topics could come from different aspects that include but are not limited to the following:
• AFS near-and-long term planning on network
• Alternative fuel transit system planning and operations
• Smart AFV routing strategies
• Interactions between users, technologies, and economy
• Integrated models to evaluate broad impacts of AFV technologies, e.g., transportation-energy-land use interactions
• Interaction of public and private sectors in driving the technologies to marketplace
• New business/operational models enabled by AFVs, e.g., vehicle-to-grid operations
Submission Method
All submissions will go through the journal’s standard peer-review process. For guidelines to prepare your manuscript and for manuscript submission, please visit http://ees.elsevier.com/trc. When submitting your manuscript, please choose “SI: AFV transport systems” for “Article Type”. This is to ensure that your submission will be considered for this special issue instead of being handled as a regular paper. All submissions will be subject to the journal’s standard peer review process. Criteria for acceptance include originality, contribution, and scientific merit.
Important Dates
Submission website opens: January 5, 2015
Submission deadline of full paper online: April 5, 2015
Feedback from first-round reviews: July 15, 2015
Feedback from second-round reviews (if indicated): October 15, 2015
Final manuscripts due: December 15, 2015
Planned publication: 2016
Inquires
All inquiries regarding this call for papers should be directed to Guest Editors, Drs. Yongxi (Eric) Huang (yxhuang@clemson.edu), Michael Kuby (mikekuby@asu.edu), and Joseph Chow (joseph.chow@ryerson.ca) or to the Editor-in-Chief, Dr. Yafeng Yin (yafeng@ce.ufl.edu).
Chow, J.Y.J., Regan, A.C., 2013. A surrogate-based multiobjective metaheuristic and network degradation simulation model for robust toll pricing. Optimization and Engineering, accepted for publication.
The model was used to generate failure scenarios for evaluating a robust toll pricing policy. The original method was devised in:
Curtis, W., Zikan, K., Sowizral, H., 2006. Random Sampling for Multivariate Bernoulli Variables. United States Patent, No. US 7,006,954 B1, Feb 28, 2006."
Chow, J.Y.J., Regan, A.C., 2013. A surrogate-based multiobjective metaheuristic and network degradation simulation model for robust toll pricing. Optimization and Engineering, accepted for publication.
This script was used to test the 5 test functions discussed in sections 3.3-3.4 in the paper above, and modified to obtain the Pareto set for a robust toll pricing problem (presented in a separate teaching document)."
"Chow, J.Y.J., 2012. Activity-based travel scenario analysis with routing problem reoptimization. Computer-Aided Civil and Infrastructure Engineering, Special Issue on Computational Methods for Advanced Transportation Planning, eds. S.T. Waller and C.R. Bhat, accepted for publication. "
Nakamura, T., Hirata, Y., Judd, K., Kilminster, D., 2007. Improved parameter estimation from noisy time series for nonlinear dynamical systems. International Journal of Bifurcation and Chaos 17(5), 1741-1752.
I modified their method to have a smoother descent using Method of Successive Averages. The purpose was to apply this method to estimate chaotic arrivals in a chaos-driven queue, which is both deterministic and unpredictable, allowing one to analyze transient properties and propagate complex queue patterns over a network. This work is in:
Chow, J.Y.J., 2013. On observable chaotic maps for queueing analysis. Transportation Research Record, accepted for publication.
Chow, J.Y.J., Regan, A.C., 2011. Resource location and relocation models with rolling horizon forecasting for wildland fire planning. INFOR 49(1), 31-43."
In this seminar, the concept is introduced along with its applicability to mathematical programming problems: linear programming, integer programming, and nonlinear programming. Each can be applied to specific transportation problems: inverse shortest path problem when travel costs are uncertain but routes are observed; inverse vehicle routing problems to infer activity-based travel behavior from a population of observed travel patterns; inverse traffic assignment problems that work well for freight modeling with incomplete information about transshipment facility parameters. A fixed point prior for a sample population is introduced for regularization. Related ongoing research in activity-based modeling, network design, and urban logistics and how they will enhance the opportunities available from urban Big Data will be discussed.
The first is to introduce flexibility in the design of transportation networks. The basic network design problem is introduced along with its limitations. The static problem is modified to a stochastic dynamic programming formulation using approximate dynamic programming solution methods from real option theory to overcome the “curse of dimensionality”. This approach allows system designs to be viewed as options over time instead of as commitments, which explicitly incorporate the value of flexibility under uncertainty.
The second is incorporating user awareness. Systems designs often neglect the behavioral responses of users. We introduce the concept of an “activity-based network design problem”, where the design of a network is not based on pre-defined OD demand, but instead outputs the demand as a consequence of the network design and the users’ inherent preferences for activities.
Ongoing research and challenges are highlighted. Foremost among these challenges are the need to design and evaluate transport service systems at an urban level (e.g. if bike-sharing were incorporated by the City of Toronto as an extended service of the TTC, how would people’s travel patterns adjust and adopt the service? How would parking prices be affected?); the need to visualize these changes in a portfolio framework (as interacting assets that change value over time visualized in a GIS platform); and the need to validate service design strategies and operational business models using mobile device-based test beds.
small network example demonstrates the potential dependency between transit systems and bike-sharing systems for the first time, and the existence of an optimal discount value for considering bundled fares. A larger bike-sharing network, BIXI, is examined in the presence of the Toronto Transit Commission (TTC) in downtown Toronto to address the question of subsidy. It is found that BIXI is operating in a relatively transit-friendly state, and subsidy by TTC to maintain a status quo in Toronto may be worth considering if the cost of subsidy is less than a conservative average reduction achieved of 2.43 units of
transit-only user cost for every 1 unit increase of bike-sharing cost.
can capture transient behavior, intermittency, steady state behavior, and complex distributions in arrival rates. These characteristics are especially desirable in many queueing applications in transportation. Earlier studies resulted in chaotic queueing models that cannot be estimated using observed arrivals. An alternative queueing model is presented along with methods to specify the model, interpret its results, and estimate its parameters. The proposed queueing model uses chaotic maps of inter-arrival times to generate arrivals so that parameters can be calibrated with observable data. A sample queue based on the ergodic logistic map is presented. To calibrate the mapping based on observed data, a joint parameter and state estimation algorithm is presented using the method of successive averages. An illustration is made with two connected queues to show how a purely deterministic queueing network can still result in a joint invariant distribution. The results offer a positive view of this method and its applicability to queueing problems, particularly in the field of transportation and dynamic network loading."