Toward Human-Centric Transportation and Energy Metrics: Influence of Mode, Vehicle Occupancy, Trip Distance, and Fuel Economy

Henao, Alejandro; Sperling, Joshua; Weigl, Dustin; Atnoorkar, Swaroop; Wilson, A. M.; Nobler, Erin; Shankari, K.; Smith, Scott

doi:10.1177/03611981221086932

Cited by 2 publications

(1 citation statement)

References 31 publications

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“…Well-being has been regarded as the object of public policy (Mokhtarian, 2019), and this is no exception in the field of transportation as some transport-related literature have also proposed that travel well-being (TWB) should be one of the goals of policy design (de Oña et al, 2016;Duarte et al, 2010). In recent years, human-centric approach has been emphasized by increasingly travel-related studies (Henao et al, 2022;Luan & Corman, 2022;Mitchell et al, 2016;Pourhashem et al, 2023). A traveler's emotional experience plays an important role in shaping the travel experience and influencing the quality of the trip (Pourhashem et al, 2023).…”

Section: Vehicle Scheduling In the Post Covid-19 Pandemicmentioning

confidence: 99%

Urban bus scheduling considering maximizing TWB under the fixed departure times: A case of Beijing

Peng,

Wang,

Yang

et al. 2023

Preprint

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Urban buses play a crucial role in urban transportation, and optimizing bus scheduling has emerged as a significant research area in transportation. However, existing studies have largely overlooked factors like travel satisfaction and subjective well-being when designing bus schedules. To bridge this gap and enhance passengers' well-being while promoting human-centric traffic management, this study employed a multiple linear regression model to establish a connection between passengers' perceived well-being and objective scheduling service attributes, such as perceived in-vehicle congestion, in-vehicle travel time, and waiting time at the station. By deriving a Travel Well-Being (TWB) index from these scheduling attributes, the study aimed to use it as a comprehensive optimization objective for bus timetable optimization. To validate the proposed optimization approach, a bus line in Beijing, China, was selected, and the results obtained using the Differential Evolution (DE) algorithm demonstrated that the scheduling model, incorporating well-being considerations, effectively increased the estimated well-being rate by 29.80% compared to the original timetable. This study contributes to establishing a human-centric and inclusive transportation system by integrating perceived subjective well-being into the optimization of bus timetables.

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Section: Vehicle Scheduling In the Post Covid-19 Pandemicmentioning

confidence: 99%

Urban bus scheduling considering maximizing TWB under the fixed departure times: A case of Beijing

Peng,

Wang,

Yang

et al. 2023

Preprint

View full text Add to dashboard Cite

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Corridor-Level Auto Occupancy in Mobility Monitoring Efforts: A Crash-Based Approach

Xu,

Dougald,

Miller

2024

Transportation Research Record: Journal of the Transportation R

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Vehicle occupancy (the number of persons per vehicle) is fundamental to evaluations of transportation system performance focused on the movement of people rather than solely vehicles. Historically, obtaining detailed estimates of occupancy has required manual (expensive) field data collection. This paper reports on a crash-based approach for estimating occupancy. Because some characteristics, such as the presence of multiple passengers aged 16 to 21, may affect the likelihood of crash involvement, the study examined ways to control possible crash bias using two methods. The first—heuristic bias correction, which entails synthesizing missing samples—is useful for smaller jurisdictions where there is no ground truth value readily available but an area-wide occupancy measure is needed. The second—statistical bias correction, which uses manual observations for calibration followed by the creation of a model—is suitable for obtaining occupancy at the corridor level. The approach is demonstrated with 12 years of fall and spring crash data for the Hampton Roads region of southeastern Virginia, U.S., representing 1.7 million people and 50,000 crashes. Testing showed estimated occupancies were within roughly 0.05 of true occupancy when corridor-specific calibration was performed. Further, the larger number of samples used for an area-wide occupancy means that heuristic bias correction is not always necessary: for cities with more than 200 samples, the difference between uncorrected and corrected occupancies was 0.02. For states that report the number of occupants for crashes with and without injury, this approach holds promise for estimating vehicle occupancy for both corridors and regions.

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Toward Human-Centric Transportation and Energy Metrics: Influence of Mode, Vehicle Occupancy, Trip Distance, and Fuel Economy

Cited by 2 publications

References 31 publications

Urban bus scheduling considering maximizing TWB under the fixed departure times: A case of Beijing

Urban bus scheduling considering maximizing TWB under the fixed departure times: A case of Beijing

Corridor-Level Auto Occupancy in Mobility Monitoring Efforts: A Crash-Based Approach

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