published journal article
The impact of residential density on vehicle usage and energy consumption
Journal of Urban Economics
Publication Date
January 1, 2009
Archives: Research Products
Preprint Journal Article
Risk Aware Reservoir Control For Safer Urban Traffic Networks
Publication Date
August 9, 2025
Author(s)
Alexander Hammerl, Wenlong Jin, Ravi Seshadri, Thomas Kjær Rasmussen, Otto Anker Nielsen
Abstract
We present a risk-aware perimeter-style controller that couples safety and efficiency targets in large, heterogeneous urban traffic networks. The network is compressed into two interacting “reservoirs” whose dynamics follow the Generalized Bathtub Model, while accidents are described by a self-exciting (Hawkes) counting process whose intensity depends on vehicle exposure, speed dispersion between reservoirs and accident clustering. Accident occurrences feed back into operations through an analytically simple degradation factor that lowers speed and discharge capacity in proportion to the live accident load. A receding-horizon policy minimizes a mixed delay-safety objective that includes a variance penalty capturing risk aversion; the resulting open-loop problem is shown to possess a bang-bang optimum whose gates switch only at accident times. This structure enables an event-triggered MPC that only re-optimizes when new accidents occur, reducing on-line computation significantly. Parameters are calibrated using OpenStreetMap data for metropolitan Copenhagen to analyze traffic dynamics during morning peak commuter demand. Monte-Carlo simulations demonstrate delay savings of up to 30% and accident reductions of up to 35% relative to an uncontrolled baseline, with a transparent trade-off governed by a single risk parameter.
Suggested Citation
Alexander Hammerl, Wenlong Jin, Ravi Seshadri, Thomas Kjær Rasmussen and Otto Anker Nielsen (2025) “Risk Aware Reservoir Control For Safer Urban Traffic Networks”. arXiv. Available at: 10.48550/arXiv.2508.06790.
working paper
Vehicle Point Cloud Reconstruction Framework for FHWA axle-based Classification using Roadside LiDAR Sensor
Publication Date
August 2, 2021
Associated Project
Author(s)
Suggested Citation
Yiqiao Li, Andre Tok, Zhe Sun, Stephen G. Ritchie and Koti Reddy Allu (2021) Vehicle Point Cloud Reconstruction Framework for FHWA axle-based Classification using Roadside LiDAR Sensor. Working Paper. Institute of Transportation Studies, Irvine. Available at: https://escholarship.org/uc/item/2jf86859.Related Publications
published journal article |
Jun 2023
LiDAR Vehicle Point Cloud Reconstruction Framework for Axle-Based Classification
IEEE Sensors Journal
research report |
Jan 2023
Investigation of LiDAR Sensing Technology to Improve Freeway Traffic Monitoring
Phd Dissertation |
Jan 2024
Investigation of LiDAR sensor for Traffic Monitoring with Emphasis on Heavy Duty Trucks
conference paper
Estimation of the time-dependency of values of travel time and its reliability from loop detector data
Proceedings of the 85th annual meeting of the transportation research board
Publication Date
January 1, 2006
Author(s)
Will Recker, Henry Liu, Xiaozheng He
Abstract
Although the effects of travel time and its reliability have been addressed in a variety of papers concerning pricing policies, most of the existing research is based on the assumption that travelersâ?? preferences are static over a given time interval, such as the morning commuting period. Here, we relax this assumption, assuming rather that travelersâ?? tastes toward the travel time and its reliability vary with time, and examine their time-dependent effects on travelerâ??s route choice decisions. We adopt a mixed logit formulation of route choice behavior as a function of travel time, reliability, and cost. To uncover the values of travel time and its reliability, we introduce an alternative approach to the use of traveler surveys to estimate the model coefficients by determining the parameter set that produces the best match between the aggregated results from the travelersâ?? route choice model and the observed time-dependent traffic volume data from loop detectors. We apply the methodology to loop detector data obtained from the California State Route 91 value-pricing project, and use a genetic algorithm to identify the parameters. The time-dependent values of travel time and values of reliability for the morning commuting period are estimated and their implications on the toll pricing policy are discussed. The results indicate that, under the time-dependent formulation, travel-time savings may be more important than uncertain travel time when departure time is close to such time constraints as work-start time.
Suggested Citation
Wilfred W. Recker, Henry X. Liu and Xiaozheng He (2006) “Estimation of the time-dependency of values of travel time and its reliability from loop detector data”, in Proceedings of the 85th annual meeting of the transportation research board, p. 27p. book/book chapter
Project Evaluation
Publication Date
October 1, 1998
Associated Project
Author(s)
Areas of Expertise
Abstract
Transportation policy making often requires evaluating a proposed discrete change, whether it be a physical investment or a new set of operating rules. Some proposals, like the rail tunnel under the English channel, are one-time capital investments with long-lasting effects. Others, like congestion pricing proposed for The Netherlands, require major behavioral and political groundwork.
Related Publications
working paper |
Mar 1999
A Simultaneous Model of Household Activity Participation and Trip Chain Generation
working paper |
Sep 1998
A Utility-Theory-Consistent System-of-Demand-Equations Approach to Household Travel Choice
working paper |
Mar 1997
Hypercongestion
Phd Dissertation |
Jun 1996
A Neuro-Genetic-Based Universally Transferable Freeway Incident Detection Framework
working paper |
Mar 1995
A Dynamic Forecasting System for Vehicle Markets with Clean-Fuel Vehicles
research report
Assessing the Charging-as-a-Service (CaaS) Model for EV Charging Deployment in California
Publication Date
March 1, 2025
Associated Project
Author(s)
Areas of Expertise
Abstract
Charging-as-a-Service (CaaS) is an innovative electric vehicle (EV) charging station model that allows customers access to EV chargers through a contract with a provider responsible for design, deployment, operations, and maintenance. Little is known about the motivations and experiences of stakeholders involved in CaaS operations, including providers, electric utilities, and customers. A grey literature review identified CaaS services, provider-described benefits, and utility-provided CaaS and charging services. Then, we conducted semi-structured interviews with 13 stakeholders to identify critical themes on interactions between stakeholders and the perceptions, challenges, and opportunities of the CaaS business model in addressing charging station needs in California. CaaS may have structural benefits to customer-owned chargers and could improve charger reliability, provide scalable solutions, and reduce customer fatigue with EV charging deployment. However, CaaS faces the same challenges present in the broader charging industry. The findings in this study can guide policymakers in supporting maintenance-related workforce development and streamlining and crafting EV charging infrastructure-informed subsidy programs. Additionally, stakeholders recommend municipal-led EV infrastructure planning and funding for chargers in disadvantaged communities. These interviews clarify the role of CaaS within the EV charging industry and confirm the need for engaged policymaker support to clear roadblocks, support investment, and educate customers about decision-making, which benefits all EV charging stakeholders.
Suggested Citation
Angela Yun and Matthew D. Dean (2025) Assessing the Charging-as-a-Service (CaaS) Model for EV Charging Deployment in California. Research Report. UC ITS. Available at: https://doi.org/10.7922/g2qj7fnw.Related Publications
published journal article
A Comparative Assessment of Travel Characteristics for Neo-Traditional Developments
Transportation Research Record: Journal of the Transportation Research Board
Publication Date
January 1, 1993
Author(s)
Abstract
The primary intent of this paper is to explore the claim that transportation benefits can be derived from neotraditional neighborhood design. Conventional transportation planning models are used as tools to evaluate the performance differences of two hypothetical street networks designed to replicate a neotraditional and a conventional suburban community. Relative transportation benefits are measured in terms of vehicle-miles traveled, average trip lengths, and congestion on links and at intersections. This comparison provides an assessment of how well the two networks in question deal with trips generated by the activities which they serve. All aspects of the modeled communities are held constant except for the actual configuration of the networks. The results of this evaluation indicate that equivalent levels of activity (defined by the land uses within the community) can produce greater congestion with conventional network structures and that corresponding average trip lengths are generally longer. The ultimate goal is to determine if one network type, because of the nature of its design, can result in a more efficient transportation system. The results indicate that neotraditional designs can improve system performance.
Suggested Citation
Michael G. McNally and Sherry Ryan (1993) “A Comparative Assessment of Travel Characteristics for Neo-Traditional Developments”, Transportation Research Record: Journal of the Transportation Research Board [Preprint], (1400). Available at: https://onlinepubs.trb.org/Onlinepubs/trr/1993/1400/1400-010.pdf. working paper
Land Use, Urban Design, and Non-Work Travel: Reproducing for Portland, Oregon, Empirical Tests from other Urban Areas
Publication Date
November 1, 1999
Author(s)
Areas of Expertise
conference paper
Material flow planning in multimodal manufacturing systems by computer simulation
2008 second asia international conference on modelling & simulation (AMS)
Publication Date
May 1, 2008
Author(s)
Mohsen Fattahi Ardakani, Fatemeh Ranaiefar, Ruzbeh Mohagheghzadeh
Suggested Citation
Mohsen Fattahi Ardakani, Fatmeh Ranaiefar and Ruzbeh Mohagheghzadeh (2008) “Material flow planning in multimodal manufacturing systems by computer simulation”, in 2008 second asia international conference on modelling & simulation (AMS). IEEE, pp. 728–733. Available at: 10.1109/ams.2008.103. conference paper
Pattern clustering and activity inference
Proceedings of the 93rd annual meeting of the transportation research board
Publication Date
January 1, 2014
Author(s)
Abstract
With the goal of developing procedures for predicting activity/travel patterns of individuals given their socio-demographic characteristics, the authors cluster individuals based on their activity patterns using a two-stage clustering technique to infer activity time windows. The two-stage technique is a combination of affinity propagation and K-means clustering methods. Activity patterns are created by segmenting daily activities into ten-minute intervals, carrying information about activity types, duration, schedule and travel distance. The authors test different combinations of two error measures: sequential alignment and agenda dissimilarity to compute the distance between each pair of patterns. In order to analyze the effectiveness of clustering on inferring activity patterns, the authors further test the prediction accuracy for two population, clustered and un-clustered. The results indicate that updating activity time windows based on the arrival time distribution of the clustered data, has higher accuracy than using those distributions with un-clustered data.