ACM Transactions on Modeling and Computer Simulation
The rapid introduction of mobile navigation aides that use real-time road network information to ... more The rapid introduction of mobile navigation aides that use real-time road network information to suggest alternate routes to drivers is making it more difficult for researchers and government transportation agencies to understand and predict the dynamics of congested transportation systems. Computer simulation is a key capability for these organizations to analyze hypothetical scenarios; however, the complexity of transportation systems makes it challenging for them to simulate very large geographical regions, such as multi-city metropolitan areas. In this article, we describe enhancements to the Mobiliti parallel traffic simulator to model dynamic rerouting behavior with the addition of vehicle controller actors and vehicle-to-controller reroute requests. The simulator is designed to support distributed-memory parallel execution using discrete event simulation and be scalable on high-performance computing platforms. We demonstrate the potential of the simulator by analyzing the imp...
Recent technological improvements have expanded the sharing economy (e.g., Airbnb, Lyft, and Uber... more Recent technological improvements have expanded the sharing economy (e.g., Airbnb, Lyft, and Uber), coinciding with a growing need for evacuation resources. To understand factors that influence sharing willingness in evacuations, we employed a multi-modeling approach using three model types: 1) four binary logit models that capture sharing scenario separately; 2) a portfolio choice model (PCM) that estimates dimensional dependency, and 3) a multi-choice latent class choice model (LCCM) that jointly estimates multiple scenarios via latent classes. We tested our approach by employing online survey data from Hurricane Irma (2017) evacuees (n=368). The multi-model approach uncovered behavioral nuances undetectable with one model. For example, the multi-choice LCCM and PCM models uncovered scenario correlation and the multi-choice LCCM found three classestransportation sharers, adverse sharers, and interested sharerswith different memberships. We suggest that local agencies consider broader sharing mechanisms across resource types and time (i.e., before, during, and after evacuations).
Technological advancements are rapidly changing traffic management in cities. Massive adoption of... more Technological advancements are rapidly changing traffic management in cities. Massive adoption of mobile devices and cloud-based applications have created new mechanisms for urban traffic control and management. Specifically, navigation applications have impacted cities in multiple ways by rerouting traffic on their streets. As different routing strategies distribute traffic differently across the city network, understanding these differences across multiple dimensions is highly relevant for policymakers. In this paper, we develop a holistic framework of indicators, called Socially-Aware Evaluation Framework for Transportation (SAEF), that will assist in understanding how traffic routing and the resultant traffic dynamics impact city metrics, with the intent of avoiding unintended consequences and adhering to city objectives. SAEF is a holistic decision framework formed as an assembled set of city performance indicators grounded in the literature. The selected indicators can be evaluated for cities of various sizes and at the urban scale. The SAEF framework is presented for four Bay Area cities, for which we compare three different routing strategies. Our intent with this work is to provide an evaluation framework that enables reflection on the consequence of policies, traffic management strategies and network changes. With an ability to model out proposed traffic management strategies, the policymaker can consider the trade-offs and potential unintended consequences.
Recent technological improvements have expanded the sharing economy (e.g., Airbnb, Lyft, and Uber... more Recent technological improvements have expanded the sharing economy (e.g., Airbnb, Lyft, and Uber), coinciding with a growing need for evacuation resources. To understand factors that influence sharing willingness in evacuations, we employed a multi-modeling approach using three model types: 1) four binary logit models that capture sharing scenario separately; 2) a portfolio choice model (PCM) that estimates dimensional dependency, and 3) a multi-choice latent class choice model (LCCM) that jointly estimates multiple scenarios via latent classes. We tested our approach by employing online survey data from Hurricane Irma (2017) evacuees (n=368). The multi-model approach uncovered behavioral nuances undetectable with one model. For example, the multi-choice LCCM and PCM models uncovered scenario correlation and the multi-choice LCCM found three classestransportation sharers, adverse sharers, and interested sharerswith different memberships. We suggest that local agencies consider broader sharing mechanisms across resource types and time (i.e., before, during, and after evacuations).
Traffic simulations are often used by city planners as a basis for predicting the impact of polic... more Traffic simulations are often used by city planners as a basis for predicting the impact of policies, plans, and operations. The complexities underpinning traffic simulations are often not described in detail yet can significantly impact the simulation outcome. Conflating underlying data for simulations is complex and hinders the interest in this type of exploration. This paper aims to elucidate critical features of traffic simulations that drive the generated metrics of the modeled urban environment. Specifically, this paper examines differences in two road graph networks for the metropolitan region of Houston, TX: a reduced network composed of 45,675 road links and an expanded network consisting of 729,753 road links. This paper will also cover collecting, refining, the feature extracting, and mapping matching real-world data to the simulated data. The traffic dynamics are generated by a simulator called Mobiliti. Two scenarios are explored: a baseline shortest travel time with 50% of the vehicles enabled to dynamically route to reduce travel time (B50), and a User Equilibrium Travel time (UET) scenario that results from a quasi-dynamic traffic assignment optimization. The resultant dynamics of these routing algorithms generate speeds and flows on the road graph links. The demand model trips are characterized by key features like travel times, delay times, and vehicle miles traveled. Validation with real-world data is presented using open-source Texas Department of Transportation data. The validation results of the various simulations provided evidence that the expanded network resulted in a more accurate simulation.
Traffic assignment is one of the key approaches used to model the congestion patterns that arise ... more Traffic assignment is one of the key approaches used to model the congestion patterns that arise in transportation networks. Since static traffic assignment does not have a notion of time dynamics, it is not designed to represent the complex dynamics of transportation networks as usage changes throughout the day. Dynamic traffic assignment methods attempt to resolve these dynamics, but require significant computational resources if modeling urban-scale regions (on the order of millions of links and vehicle trips) and often take days of compute time to complete the optimization. The ∗Research Scientist at Lawrence Berkeley National Laboratory †Graduate Student Researcher at University of California, Berkeley ‡Researcher at University of California, Berkeley §Graduate Student Researcher at University of California, Berkeley ¶Researcher at University of California, Berkeley ‖Researcher at Lawrence Berkeley National Laboratory ∗∗Professor at University of California, Berkeley ††Executiv...
ACM Transactions on Modeling and Computer Simulation
The rapid introduction of mobile navigation aides that use real-time road network information to ... more The rapid introduction of mobile navigation aides that use real-time road network information to suggest alternate routes to drivers is making it more difficult for researchers and government transportation agencies to understand and predict the dynamics of congested transportation systems. Computer simulation is a key capability for these organizations to analyze hypothetical scenarios; however, the complexity of transportation systems makes it challenging for them to simulate very large geographical regions, such as multi-city metropolitan areas. In this article, we describe enhancements to the Mobiliti parallel traffic simulator to model dynamic rerouting behavior with the addition of vehicle controller actors and vehicle-to-controller reroute requests. The simulator is designed to support distributed-memory parallel execution using discrete event simulation and be scalable on high-performance computing platforms. We demonstrate the potential of the simulator by analyzing the imp...
Recent technological improvements have expanded the sharing economy (e.g., Airbnb, Lyft, and Uber... more Recent technological improvements have expanded the sharing economy (e.g., Airbnb, Lyft, and Uber), coinciding with a growing need for evacuation resources. To understand factors that influence sharing willingness in evacuations, we employed a multi-modeling approach using three model types: 1) four binary logit models that capture sharing scenario separately; 2) a portfolio choice model (PCM) that estimates dimensional dependency, and 3) a multi-choice latent class choice model (LCCM) that jointly estimates multiple scenarios via latent classes. We tested our approach by employing online survey data from Hurricane Irma (2017) evacuees (n=368). The multi-model approach uncovered behavioral nuances undetectable with one model. For example, the multi-choice LCCM and PCM models uncovered scenario correlation and the multi-choice LCCM found three classestransportation sharers, adverse sharers, and interested sharerswith different memberships. We suggest that local agencies consider broader sharing mechanisms across resource types and time (i.e., before, during, and after evacuations).
Technological advancements are rapidly changing traffic management in cities. Massive adoption of... more Technological advancements are rapidly changing traffic management in cities. Massive adoption of mobile devices and cloud-based applications have created new mechanisms for urban traffic control and management. Specifically, navigation applications have impacted cities in multiple ways by rerouting traffic on their streets. As different routing strategies distribute traffic differently across the city network, understanding these differences across multiple dimensions is highly relevant for policymakers. In this paper, we develop a holistic framework of indicators, called Socially-Aware Evaluation Framework for Transportation (SAEF), that will assist in understanding how traffic routing and the resultant traffic dynamics impact city metrics, with the intent of avoiding unintended consequences and adhering to city objectives. SAEF is a holistic decision framework formed as an assembled set of city performance indicators grounded in the literature. The selected indicators can be evaluated for cities of various sizes and at the urban scale. The SAEF framework is presented for four Bay Area cities, for which we compare three different routing strategies. Our intent with this work is to provide an evaluation framework that enables reflection on the consequence of policies, traffic management strategies and network changes. With an ability to model out proposed traffic management strategies, the policymaker can consider the trade-offs and potential unintended consequences.
Recent technological improvements have expanded the sharing economy (e.g., Airbnb, Lyft, and Uber... more Recent technological improvements have expanded the sharing economy (e.g., Airbnb, Lyft, and Uber), coinciding with a growing need for evacuation resources. To understand factors that influence sharing willingness in evacuations, we employed a multi-modeling approach using three model types: 1) four binary logit models that capture sharing scenario separately; 2) a portfolio choice model (PCM) that estimates dimensional dependency, and 3) a multi-choice latent class choice model (LCCM) that jointly estimates multiple scenarios via latent classes. We tested our approach by employing online survey data from Hurricane Irma (2017) evacuees (n=368). The multi-model approach uncovered behavioral nuances undetectable with one model. For example, the multi-choice LCCM and PCM models uncovered scenario correlation and the multi-choice LCCM found three classestransportation sharers, adverse sharers, and interested sharerswith different memberships. We suggest that local agencies consider broader sharing mechanisms across resource types and time (i.e., before, during, and after evacuations).
Traffic simulations are often used by city planners as a basis for predicting the impact of polic... more Traffic simulations are often used by city planners as a basis for predicting the impact of policies, plans, and operations. The complexities underpinning traffic simulations are often not described in detail yet can significantly impact the simulation outcome. Conflating underlying data for simulations is complex and hinders the interest in this type of exploration. This paper aims to elucidate critical features of traffic simulations that drive the generated metrics of the modeled urban environment. Specifically, this paper examines differences in two road graph networks for the metropolitan region of Houston, TX: a reduced network composed of 45,675 road links and an expanded network consisting of 729,753 road links. This paper will also cover collecting, refining, the feature extracting, and mapping matching real-world data to the simulated data. The traffic dynamics are generated by a simulator called Mobiliti. Two scenarios are explored: a baseline shortest travel time with 50% of the vehicles enabled to dynamically route to reduce travel time (B50), and a User Equilibrium Travel time (UET) scenario that results from a quasi-dynamic traffic assignment optimization. The resultant dynamics of these routing algorithms generate speeds and flows on the road graph links. The demand model trips are characterized by key features like travel times, delay times, and vehicle miles traveled. Validation with real-world data is presented using open-source Texas Department of Transportation data. The validation results of the various simulations provided evidence that the expanded network resulted in a more accurate simulation.
Traffic assignment is one of the key approaches used to model the congestion patterns that arise ... more Traffic assignment is one of the key approaches used to model the congestion patterns that arise in transportation networks. Since static traffic assignment does not have a notion of time dynamics, it is not designed to represent the complex dynamics of transportation networks as usage changes throughout the day. Dynamic traffic assignment methods attempt to resolve these dynamics, but require significant computational resources if modeling urban-scale regions (on the order of millions of links and vehicle trips) and often take days of compute time to complete the optimization. The ∗Research Scientist at Lawrence Berkeley National Laboratory †Graduate Student Researcher at University of California, Berkeley ‡Researcher at University of California, Berkeley §Graduate Student Researcher at University of California, Berkeley ¶Researcher at University of California, Berkeley ‖Researcher at Lawrence Berkeley National Laboratory ∗∗Professor at University of California, Berkeley ††Executiv...
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