working paper

Structural Models of the Effects of the Commute Trip on Travel and Activity Participation

Publication Date

November 1, 1991

Associated Project

Author(s)

Thomas Golob, Ram Pendyala

Working Paper

UCI-ITS-WP-91-15, UCI-ITS-AS-WP-91-1

Areas of Expertise

Abstract

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.

Suggested Citation
Thomas F. Golob and Ram M. Pendyala (1991) Structural Models of the Effects of the Commute Trip on Travel and Activity Participation. Working Paper UCI-ITS-WP-91-15, UCI-ITS-AS-WP-91-1. Institute of Transportation Studies, Irvine. Available at: https://escholarship.org/uc/item/3hq9m5hp.

published journal article

Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisions

IEEE Transactions on Intelligent Transportation Systems

Publication Date

July 1, 2022

Author(s)

Shih-Yuan Yu, Arnav Vaibhav Malawade, Deepan Muthirayan, Pramod P. Khargonekar, Mohammad Al Faruque

Abstract

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).

Suggested Citation
Shih-Yuan Yu, Arnav Vaibhav Malawade, Deepan Muthirayan, Pramod P. Khargonekar and Mohammad Abdullah Al Faruque (2022) “Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisions”, IEEE Transactions on Intelligent Transportation Systems, 23(7), pp. 7941–7951. Available at: 10.1109/TITS.2021.3074854.

published journal article

Globally Optimal Assignment Algorithm for Collective Object Transport Using Air–Ground Multirobot Teams

IEEE Transactions on Control Systems Technology

Publication Date

January 1, 2024

Author(s)

Tatsuya Miyano, Justin Romberg, Magnus Egerstedt

Abstract

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.

Suggested Citation
Tatsuya Miyano, Justin Romberg and Magnus Egerstedt (2024) “Globally Optimal Assignment Algorithm for Collective Object Transport Using Air–Ground Multirobot Teams”, IEEE Transactions on Control Systems Technology, 32(1), pp. 258–265. Available at: 10.1109/TCST.2023.3291880.

working paper

Impacts of Highway Congestion on Freight Operations: Perceptions of Trucking Industry Managers

Abstract

To better understand how road congestion adversely affects trucking operations, we surveyed approximately 1200 managers of all types of trucking companies operating in California. More than 80% of these managers consider traffic congestion on freeways and surface streets to be either a “somewhat serious” or “critically serious” problem for their business. A structural equations model (SEM) is estimated on these data to determine how five aspects of the congestion problem differ across sectors of the trucking industry. The five aspects were slow average speeds, unreliable travel times, increased driver frustration and morale, higher fuel and maintenance costs, and higher costs of accidents and insurance. The model also simultaneously estimates how these five aspects combine to predict the perceived overall magnitude of the problem. Overall, congestion is perceived to be a more serious problem by managers of trucking companies engaged in intermodal operations, particularly private and for-hire trucking companies serving airports and private companies serving rail terminals. Companies specializing in refrigerated transport also perceive congestion to be a more serious overall problem, as do private companies engaged in LTL operations. The most problematic aspect of congestion is unreliable travel times, followed by driver frustration and morale, then by slow average speeds. Unreliable travel times are a significantly more serious problem for intermodal air operations. Driver frustration and morale attributable to congestion is perceived to be more of a problem by managers of long-haul carriers and tanker operations. Slow average speeds are also more of a concern for airport and refrigerated operations.

published journal article

Analysis and visualization method for understanding the voltage effect of distributed energy resources on the electric power system

Electric Power Systems Reserch

Publication Date

January 1, 2012

Author(s)

Allie E. Auld, Jack Brouwer, Scott Samuelsen
Suggested Citation
Allie E. Auld, Jack Brouwer and G. Scott Samuelsen (2012) “Analysis and visualization method for understanding the voltage effect of distributed energy resources on the electric power system”, Electric Power Systems Reserch, 82(1), pp. 44–53. Available at: 10.1016/j.epsr.2011.08.012.

published journal article

Selective vehicle routing problems under uncertainty without recourse

Transportation Research Part E: Logistics and Transportation Review

Publication Date

February 1, 2014
Suggested Citation
Mahdieh Allahviranloo, Joseph Y.J. Chow and Will W. Recker (2014) “Selective vehicle routing problems under uncertainty without recourse”, Transportation Research Part E: Logistics and Transportation Review, 62, pp. 68–88. Available at: 10.1016/j.tre.2013.12.004.

published journal article

Security analysis for fixed-time traffic control systems

Transportation Research Part B: Methodological

Publication Date

September 1, 2020

Abstract

Wireless communication is being used as an enabling technology with traditional fixed traffic control systems in this transitional era toward Intelligent Transportation Systems (ITS). Unfortunately, major security concerns have arisen with respect to ever-increasing complexity and interconnectivity, and a noticeable lack of attention for security in these systems. Addressing concerns is a colossal challenge as it requires thorough development and formal analysis of a system model with respect to security. To tackle this challenge, we present a novel formal attack modeling and impact analysis methodology based on the Link Queue Model (LQM) of traffic flow inside a double ring road network, which is equivalent to a grid network with homogeneous links. We develop attack models as functions of tampered traffic control settings (e.g., green time ratios, cycle length, retaining ratios) with outputs equivalent to mobility impacts on the traffic network (e.g., time until system reaches state convergence, asymptotic average network flow). Further, for a given attack model, we define and identify vulnerable states: states that are critical to protect because they lead to negative impacts under the given attack model. Using our methodology we found that for certain vulnerable states, after only a few cycles of tampered control settings an attacker could cause a real impact of 1.5x speed-up in gridlock state convergence or 37%-99% drop in the asymptotic average flow rate. These results imply potentially drastic financial costs for cities and all involved drivers if similar attacks were performed on a real traffic control system. (C) 2020 Elsevier Ltd. All rights reserved.

Suggested Citation
Anthony Lopez, Wenlong Jin and Mohammad Abdullah Al Faruque (2020) “Security analysis for fixed-time traffic control systems”, Transportation Research Part B: Methodological, 139, pp. 473–495. Available at: 10.1016/j.trb.2020.07.002.

published journal article

Public Transit Performance Evaluation: Application to Section 15 Data

Transportation Research Record

Publication Date

January 1, 1983

Author(s)

Shirley C Anderson, Gordon (Pete) Fielding

Abstract

Performance indcators are quantitative measures that enable managers and policymakers to monitor the current position of an agency and outline strategies to improve performance. Because public services have many different dimensions of performance, a large number of performance indicators are normally used. In this paper a conceptual model is used to help select a few performance indicators that represent all the important performance concepts. Data were obtained from a national sample of 311 urban bus transit systems in the first year that data were reported under Section 15 of the Urban Mass Transportation Act of 1965, as amended. The steps in the performarice-evaluatlon procedure Involve defining a conceptual model of performance and designing a balancod set of performance indicators that represent all performance concepts. Factor analysis is then used to select the indicators that best represent all dimensions of performance. This smell, representative set of performance Indicators is used to analyze performance and to establish peer-group rankings.

Suggested Citation
Shirley C Anderson and Gordon J. Fielding (1983) “Public Transit Performance Evaluation: Application to Section 15 Data”, Transportation Research Record, (947), pp. 1--6. Available at: https://onlinepubs.trb.org/Onlinepubs/trr/1983/947/947-001.pdf.

published journal article

Marketing implications of perceptions of transit

TRANSPORTATION ENGINEERING JOURNAL OF ASCE

Publication Date

January 1, 1982

Author(s)

Ww Recker, Hj Schuler
Suggested Citation
Ww Recker and Hj Schuler (1982) “Marketing implications of perceptions of transit”, TRANSPORTATION ENGINEERING JOURNAL OF ASCE, 108(6), pp. 650–661.

research report

Spatial analysis of bicycling ridership patterns from bias-corrected crowdsourced data

Abstract

This study leverages big data from the Strava Metro app, integrated with official count data from the Orange County Transportation Authority, to analyze spatial patterns of bicycling ridership in Orange County, California. By applying bias correction techniques to crowdsourced data and incorporating land use and socioeconomic covariates, the study generate a comprehensive map of ridership volumes across the region. The study’s analysis reveals significant spatial autocorrelation in cycling activity, with distinct patterns between coastal and inland areas. Coastal regions exhibit strong High-High clusters, indicating concentrated cycling activity, while inland areas show a more varied pattern with Low-High clusters and isolated High-High pockets. These findings demonstrate the potential of bias-corrected crowdsourced data to inform targeted infrastructure planning in both urban and suburban contexts. By identifying areas of high cycling demand and potential growth, this methodology provides valuable insights for policymakers and urban planners to enhance cycling infrastructure and promote sustainable transportation in diverse geographic settings, from coastal cities to inland communities.

Suggested Citation
Avipsa Roy and Ghangshin Lee (2025) Spatial analysis of bicycling ridership patterns from bias-corrected crowdsourced data. Final Report PSR-22-24-TO 069. PSR / ITS-Irvine. Available at: https://www.metrans.org/assets/research/psr-22-24%20to-069%20avipsa%20roy.pdf.