conference paper
conference paper
Providing quality transit service to travelers in low-density areas, particularly travelers without personal vehicles, is a constant challenge for transit agencies. The advent of fully-autonomous vehicles (AVs) and their inclusion in mobility service fleets may allow transit agencies to offer travelers better service and/or reduce their own capital and operational costs. This study focuses on the problem of allocating resources between transit patterns and operating (or subsidizing) shared-use AV mobility services (SAMSs) in a large metropolitan area. To address this problem, a bi-level mathematical programming formulation and solution algorithm are presented for the joint transit network redesign and SAMS fleet size determination problem (JTNR-SFSDP). The upper-level problem modifies a transit network frequency setting problem (TNFSP) formulation via incorporating SAMS fleet size as a decision variable. The lower-level problem consists of a dynamic combined mode choiceâ??traveler assignment problem (DCMC-TAP) formulation. The solution procedure involves solving the upper-level problem using a nonlinear programming solver and solving the lower-level problem using an iterative agent-based simulation-assignment approach. To illustrate the effectiveness of the modeling framework, this study uses traveler demand from Chicago along with the regionâ??s existing multimodal transit network. The results indicate the ability of the solution procedure to solve the bi-level JTNR-SFSDP. Moreover, computational results indicate significant traveler benefits associated with optimizing the joint design of multimodal transit networks and SAMS fleets.
research report
Project included three major parts: 1) field measurements of particulate matter in five urban areas, 2) laboratory modeling of flow and dispersion within model urban areas, and 3) numerical modeling. Project website and database are located at http://emissions.engr.ucr.edu/.
research report |
Aug 2010
published journal article |
Aug 2011
published journal article |
Jan 2013
conference paper
conference paper
working paper
Proper prediction of traffic flow parameters is an essential component of any proactive traffic control system and one of the pillars of advanced management of dynamic traffic networks. In this paper, we present a new short term traffic flow prediction system based on an advanced Time Delay Neural Network (TDNN) model, the structure of which is optimized using a Genetic Algorithm (GA). After presentation of the model’s development, its performance is validated using both simulated and real traffic flow data obtained from the California Testbed in Orange County, California. The model predicts flow and occupancy values at a given freeway site based on contributions from their recent temporal profile as well the spatial contribution from neighboring sites. Both temporal and spatial effects were found essential for proper prediction. An in-depth investigation of the variables pertinent to traffic flow prediction was conducted examining the extent of the “look-back” interval, the extent of prediction in the future, the extent of spatial contribution, the resolution of the input data, and their effects on prediction accuracy. Results obtained indicate that the prediction errors vary inversely with the extent of the spatial contribution, and that the inclusion of three loop stations in both directions of the subject station is sufficient for practical purposes. Also, the longer the extent of prediction, the more the predicted values tend toward the mean of the actual, for which case the optimal look-back interval also shortens. Interestingly, it was found that coarser data resolution is better for longer extents of prediction. The implication is that the level of data aggregation/resolution should be comparable to the prediction horizon for best accuracy. The model performed acceptably using both simulated and real data. The model also showed potential to be superior to such other well-known neural network models as the Multi layer Feed-forward (MLF) when applied to the same problem. Keywords: Traffic Flow Prediction, Neural Networks, Genetic Algorithms, Traffic Management.
research report |
Sep 2001
research report |
Sep 1993
research report |
Sep 1997
working paper |
Jun 1999
Phd Dissertation |
Jun 1996
working paper
Travel demand is viewed as being derived from the demand for out-of-home activities. The journey to work can have a significant impact on the travel and activity patterns of workers and other household members. The objective of this research is to model the relationships between travel and activity participation and examine how these relationships are influenced by the time a worker spends commuting between home and his or her worksite. Causal hypotheses are tested using data from approximately 140 workers who responded to two waves of a panel survey collected as part of the State of California Telecommuting Pilot Project. These data contain detailed descriptions of all travel by the survey respondents over three working days in each of two years, 1988 and 1989. A structural equations model is specified in which the durations of four exhaustive categories of out-of-home activities – work, personal business, shopping and social/recreation -generate needs for time spent traveling, and durations and travel times are interrelated in a complex causal structure. The effects of the reduction in travel times for work by telecommuters in the second wave of the panel are captured in terms of additional structural parameters. Results indicate that telecommuting leads directly to increases in shopping activities and decreases in travel for social/recreational activities, and leads indirectly to changes in travel for all purposes. A general modeling framework in which activities and travel relationships can be studied is also discussed.
working paper |
Aug 1990
working paper |
May 1990
working paper |
Sep 1991
working paper |
Jun 1991
working paper |
Sep 1991
published journal article
published journal article
There is considerable evidence that evaluating the subjective risk level of driving decisions can improve the safety of Autonomous Driving Systems (ADS) in both typical and complex driving scenarios. In this paper, we propose a novel data-driven approach that uses scene-graphs as intermediate representations for modeling the subjective risk of driving maneuvers. Our approach includes a Multi-Relation Graph Convolution Network, a Long-Short Term Memory Network, and attention layers. To train our model, we formulate subjective risk assessment as a supervised scene classification problem. We evaluate our model on both synthetic lane-changing datasets and real-driving datasets with various driving maneuvers. We show that our approach achieves a higher classification accuracy than the state-of-the-art approach on both large (96.4% vs. 91.2%) and small (91.8% vs. 71.2%) lane-changing synthesized datasets, illustrating that our approach can learn effectively even from small datasets. We also show that our model trained on a lane-changing synthesized dataset achieves an average accuracy of 87.8% when tested on a real-driving lane-changing dataset. In comparison, the state-of-the-art model trained on the same synthesized dataset only achieved 70.3% accuracy when tested on the real-driving dataset, showing that our approach can transfer knowledge more effectively. Moreover, we demonstrate that the addition of spatial and temporal attention layers improves our model’s performance and explainability. Finally, our results illustrate that our model can assess the risk of various driving maneuvers more accurately than the state-of-the-art model (86.5% vs. 58.4%, respectively).
published journal article
We consider the problem of collectively transporting multiple objects using air–ground multirobot teams. The objective is to find the optimal matching between the objects and aerial/ground robots that minimizes the energy of the overall system. We reveal the local optimality criteria for this combinatorial problem and prove that combining a branch and bound algorithm with a negative-cycle canceling algorithm (NCCA) yields an efficient algorithm that provides the globally optimal solution of the problem. Numerical experiments demonstrate the performance on practical problems.