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Paper

Would Being Driven by Others Affect the Value of Travel Time? Ridehailing as an Analogy for Automated Vehicles

 
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Publication: Transportation
Volume: 46
Pages: 2103–2116
Publication Date: 2019
Summary:

It is widely believed that vehicle automation will change how travelers perceive the value of travel time (VoTT), but the magnitude of this effect is still unknown. This study investigates how highly automated vehicles (AVs) may affect VoTT, using an existing mode—ridehailing services (RHS)—as an analogy for AVs.

Both AVs and RHS relieve travelers from the effort of driving and allow them to participate in other activities while traveling. In a stated choice experiment, respondents chose between driving a personal vehicle or taking an RHS, with each mode characterized by a cost and travel time.

Analysis results using a mixed logit model indicated that the VoTT was 13% lower when being driven in an RHS than when driving a personal car. We also told half the respondents (randomly selected) that the RHS was driverless; and for half (also randomly selected) we explicitly mentioned the ability to multitask while traveling in an RHS. Mentioning multitasking explicitly led to a much lower VoTT, approximately half that of driving oneself. However, the VoTT in a driverless RHS was 15% higher than when driving a personal car, which may reflect a lack of familiarity and comfort with driverless technology at present.

These results suggest sizable reductions in VoTT for travel in future AVs, and point to the need for caution in making forecasts based on consumers’ current perceptions of AV technology.

Authors: Dr. Andisheh Ranjbari, Jingya Gao, Don MacKenzie
Recommended Citation:
Gao, J., Ranjbari, A. & MacKenzie, D. Would being driven by others affect the value of travel time? Ridehailing as an analogy for automated vehicles. Transportation 46, 2103–2116 (2019). https://doi.org/10.1007/s11116-019-10031-9
Paper

Evaluating Traffic Impacts of Permitting Trucks in Transit-Only Lanes

 
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Publication: Transportation Research Record: Journal of the Transportation Research Board
Publication Date: 2021
Summary:

With ongoing population growth and rapid development in cities, the demand for goods and services has seen a drastic increase. Consequently, transportation planners are searching for new ways to better manage the flow of traffic on existing facilities, and more efficiently utilize available and unused capacity. In this research, a lane management strategy that allows freight vehicles to use bus-only lanes is empirically evaluated in an urban setting. This paper presents an analysis of data that was collected to evaluate the operational impacts of the implementation of a freight and transit (FAT) lane, and to guide the development of future FAT lane projects by learning from the case study in Seattle, U.S. The video data was converted to vehicle counts, which were analyzed to understand the traffic impacts and used to construct a discrete choice model. The analysis shows that transit buses used the FAT lane 96% of the time, and authorizing trucks to use the lane did not affect that lane choice. Trucks used the FAT lane, but their utilization decreased with increasing numbers of buses in the FAT lane. Instead of higher rates of trucks, unauthorized vehicles, such as passenger cars and work vans, increasingly used the FAT lane during congestion. As a result of their differing schedule patterns, trucks and buses used the FAT lane at complementary times and trucks showed relatively low volumes in the FAT lane. Overall, the results are promising for a lane management strategy that may improve freight system performance without reducing transit service quality.

Recommended Citation:
Gunes, S., Goodchild, A., Greene, C., & Nemani, V. (2021). Evaluating Traffic Impacts of Permitting Trucks in Transit-Only Lanes. Transportation Research Record. https://doi.org/10.1177/03611981211031888
Paper

Impact of Transit Network Layout on Resident Mode Choice

 
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Publication: Mathematical Problems in Engineering
Volume: 4
Publication Date: 2013
Summary:
This study reviews the impact of public transit network layout (TNL) on resident mode choice. The review of TNL as a factor uses variables divided into three groups: a variable set without considering the TNL, one considering TNL from the zone level, and one considering TNL from the individual level. Using Baoding’s travel survey data, a Multinomial Logit (MNL) model is used, and the parameter estimation result shows that TNL has significant effect on resident mode choice. Based on parameter estimation, the factors affecting mode choice are further screened. The screened variable set is regarded as the input data to the BP neural network’s training and forecasting. Both forecasting results indicate that introducing TNL can improve the performance of mode choice forecasting.

 

 

Authors: Dr. Ed McCormack, Jian Gao, Peng Zhao, Chengxiang Zhuge, Hui Zhang
Recommended Citation:
Gao, J., Zhao, P., Zhuge, C., Zhang, H., & McCormack, E. D. (2013). Impact of Transit Network Layout on Resident Mode Choice. Mathematical Problems in Engineering, 2013.