Modeling and Analysis of Excess Commuting with Trip Chains

Abstract

Commuting, like other types of human travel, is complex in nature, such as trip-chaining behavior involving making stops of multiple purposes between two anchors. According to the 2001 National Household Travel Survey, about half of weekday U.S. workers made a stop during their commute. In excess commuting studies that examine a region’s overall commuting efficiency, commuting is, however, simplified as nonstop travel from homes to jobs. This research fills this gap by proposing a trip-chaining-based model to integrate trip-chaining behavior into excess commuting. Based on a case study of the Tampa Bay region of Florida, this research finds that traditional excess commuting studies underestimate both actual and optimal commute and overestimate excess commuting. For chained commuting trips alone, for example, the mean minimum commute time is increased by 70 percent from 5.48 minutes to 9.32 minutes after trip-chaining is accounted for. The gaps are found to vary across trip-chaining types by a disaggregate analysis by types of chain activities. Hence, policymakers and planners are cautioned with regards to omitting trip-chaining behavior in making urban transportation and land use policies. In addition, the proposed model can be adopted to study the efficiency of nonwork travel.

通勤, 同其它人类活动一样, 本质上非常复杂。例如, 出行链行为, 涉及到两点之间出于不同目的而做出的短暂停留。根据2001年全美国家庭的出行调查, 大约有一半上班族会在通勤过程中做一次短暂停留。过度通勤研究区域整体通勤效率, 但是把通勤简化为从住处到工作单位的无间断出行。为了填补这个空白, 本研究开发了一个融合出行链行为和过度通勤的出行链模型。基于美国弗罗里达州坦帕湾地区的案例, 本研究发现, 传统的过度通勤研究, 低估了实际最佳通勤、高估了过度通勤。例如, 如果考虑通勤出行链, 平均最小通勤时间提高了70%（从5.48分钟增加到9.32分钟）。通过对不同链活动的非集计分析, 发现不同类型出行链是不同的。因此, 在制定城市交通和土地利用政策时, 政策制定者和规划人员应避免对出行链行为的忽视。另外, 本文的模型也可以用于研究非工作出行的效率。

El viaje pendular al trabajo, como otros tipos de travesía humana, es complejo por naturaleza, como un comportamiento de viaje encadenado que implica paradas con múltiples propósitos entre dos terminales de transporte. De acuerdo con el Estudio Nacional sobre el Viaje Familiar, cerca de la mitad de los trabajadores de los EE.UU. que hacen el desplazamiento laboral hebdomadario ha efectuado una parada durante su viaje pendular. Sin embargo, en los estudios de movimientos pendulares en exceso que examinan la eficiencia del movimiento pendular de una región, este tipo de viaje generalmente es simplificado como viaje sin paradas desde las casas hasta el lugar del trabajo. Esta investigación subsana esta omisión proponiendo un modelo basado en el viaje encadenado para integrar el comportamiento del viaje encadenado dentro del viaje pendular en exceso. Con base en un estudio de caso de la región de Tampa Bay, en Florida, esta investigación encuentra que los estudios de viaje pendular en exceso subestiman el desplazamiento real y óptimo y sobreestiman el movimiento pendular en exceso. Para los viajes pendulares encadenados solos, por ejemplo, la media mínima del tiempo de la conmutación está incrementada en el 70 por ciento de 5.48 minutos a 9.32 minutos, luego de que el viaje encadenado es tomado en cuenta. Por un análisis desagregado por tipos de actividades de la cadena, se descubre que las brechas varían a través de los tipos de encadenamiento de los viajes. Por eso, a los encargados de formular políticas y a los planificadores se les previene en lo que concierne a omitir el comportamiento del viaje encadenado cuando diseñen políticas urbanas de transporte y uso del suelo. Además, el modelo propuesto puede adoptarse para estudiar la eficiencia de los viajes no relacionados con trabajo.

Key Words:

• excess commuting
• jobs–housing balance
• linear programming
• nonwork travel
• trip-chaining

关键词：过度通勤:

• 工作-家庭的平衡
• 线性规划
• 非工作出行
• 出行链。

Palabras clave::

• balance empleo–vivienda
• programación lineal
• viaje encadenado
• viaje pendular en exceso
• viaje no laboral

Acknowledgment

Data, comments, and help provided by Dr. Chunyu Lu from AECOM are gratefully acknowledged. We also thank the editor and the anonymous referees for their valuable comments that greatly improved the article.

Notes

1 A TAZ is a special geographic area delineated by state or local transportation officials for tabulating transportation data such as commuting statistics. It is usually smaller than a census tract and bigger than a census block group. The Tampa Bay region, for example, has 567 census tracts, 1,574 TAZs, and 1,602 census block groups.

2 Of the five-hour computation time, 287 minutes were used to initiate variables, load data, and set up model constraints, and the remaining 13 minutes were spent on solving the objective function in Equation 8(8) $$T_{\mathit{min}}:=\underset{\left\{x_{\mathit{cijk}}\right\}}{\min }{\sum }_{\mathrm{c}\in \mathcal{C}}{\sum }_{i,j\in \mathcal{I}}{\sum }_{k=1}^{\left|c\right|-1}{d}_{ij}{x}_{\mathit{cijk}}$$(8) .

3 Duration of the stop is not considered because of data unavailability.

4 In fact, this consistent trend in distance is also observed for other metrics. Therefore, for simplicity, only commuting time is discussed hereafter.

Additional information

Funding

This research was supported by the Center for Transportation Equity, Decisions & Dollars via Project No. 019-26 and the U.S. National Science Foundation under Grant CNS #1638355.

Notes on contributors

Yujie Hu

YUJIE HU is an Assistant Professor in the Department of Geography at the University of Florida, Gainesville, FL 32611. E-mail: [email protected]. He served as co-corresponding author for this article. His research interests are urban transportation, spatial networks, human mobility, and accessibility.

Xiaopeng Li

XIAOPENG LI is an Associate Professor in the Department of Civil and Environmental Engineering at the University of South Florida, Tampa, FL 33620. E-mail: [email protected]. He served as co-corresponding author for this article. His research interests include emerging technologies (e.g., connected and automated vehicles, modular vehicles, electric vehicles, shared mobility) in transportation network analysis and traffic control.