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2. Passengers' Travel Behavior in Response to Unplanned Transit Disruptions

Author

Golshani, Nima, Rahimi, Ehsan, Shabanpour, Ramin, Mohammadian, Kouros, Auld, Joshua, and Ley, Hubert

Subjects
Economics - General Economics
Abstract

Public transit disruption is becoming more common across different transit services, which can have a destructive influence on the resiliency and reliability of the transportation system. Utilizing a recently collected data of transit users in the Chicago Metropolitan Area, the current study aims to analyze how transit users respond to unplanned service disruption and disclose the factors that affect their behavior.

Published
2020

18. Contributors

Author

Antoniou, Constantinos, primary, Auld, Joshua, additional, Aultman-Hall, Lisa, additional, Axhausen, Kay W., additional, Balusu, Suryaprasanna Kumar, additional, Belgiawan, Prawira F., additional, Bhat, Chandra R., additional, Bönisch, Lisa, additional, Bourdeau, Jean-Simon, additional, Brändle, Norbert, additional, Briem, Lars, additional, Calastri, Chiara, additional, Chaniotakis, Emmanouil, additional, Cherchi, Elisabetta, additional, Chikaraishi, Makoto, additional, Chlond, Bastian, additional, Daisy, Naznin Sultana, additional, Davis, Adam W., additional, Diana, Marco, additional, Djavadian, Shadi, additional, Dragaschnig, Melitta, additional, Durán-Rodas, David, additional, Eluru, Naveen, additional, Enam, Annesha, additional, Farooq, Bilal, additional, Fujiwara, Akimasa, additional, Fukui, Noriko, additional, Fukuyama, Sachiyo, additional, Gärling, Tommy, additional, Golshani, Nima, additional, Goulias, Konstadinos G., additional, Gren, Mateusz, additional, Heilig, Michael, additional, Ilahi, Anugrah, additional, Jiang, Ying, additional, Kagerbauer, Martin, additional, Kamargianni, Maria, additional, Kirn, Michael, additional, Kolarova, Viktoriya, additional, Koller, Hannes, additional, Konduri, Karthik C., additional, Lehner, Stephan, additional, Liu, Chengxi, additional, Liu, Lei, additional, Maness, Michael, additional, McBride, Eizabeth C., additional, Meloni, Italo, additional, Miller, Eric J., additional, Millward, Hugh, additional, Mohammadian, Abolfazl, additional, Mohammadian, Kouros, additional, Mokhtarian, Patricia L., additional, Morency, Catherine, additional, Peer, Stefanie, additional, Pendyala, Ram M., additional, Perez Barbosa, David, additional, Pinjari, Abdul Rawoof, additional, Piras, Francesco, additional, Pirra, Miriam, additional, Plötz, Patrick, additional, Polydoropoulou, Amalia, additional, Sengupta, Raja, additional, Shabanpour, Ramin, additional, Shamshiripour, Ali, additional, Sheela, Parvathy Vinod, additional, Shiftan, Yoram, additional, Singleton, Patrick A., additional, Sottile, Eleonora, additional, Soylu, Tamer, additional, Stathopoulos, Amanda, additional, Steck, Felix, additional, Susilo, Yusak O., additional, Tahlyan, Divyakant, additional, Ullman, Hannah, additional, Vasquez, Rafael, additional, Verreault, Hubert, additional, von Behren, Sascha, additional, Vortisch, Peter, additional, Wang, Yinhai, additional, Wulfhorst, Gebhard, additional, Yip, Grace, additional, Zhang, Jingyue, additional, Zhang, Linghan, additional, and Zhang, Junyi, additional

Published
2020
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27. A data-driven approach to characterize the impact of connected and autonomous vehicles on traffic flow.

Author

Parsa, Amir Bahador, Shabanpour, Ramin, Mohammadian, Abolfazl, Auld, Joshua, and Stephens, Thomas

Subjects
*TRAFFIC flow, *AUTONOMOUS vehicles, *RANDOM forest algorithms, *MACHINE learning, *METROPOLITAN areas, *DEPENDENT variables
Abstract

This study presents a model to characterize changes in network traffic flows as a result of implementing connected and autonomous vehicle (CAV) technology based on traffic network and built-environment characteristics. To develop such a model, first, the POLARIS agent-based modeling platform is used to predict changes in average daily traffic (ADT) under CAV scenario in the road network of Chicago metropolitan area as the dependent variable of the model. Second, a comprehensive set of variables and indicators representing network characteristics and urban structure patterns are generated. Finally, three machine learning techniques, namely, K-Nearest neighbors, Random Forest, and eXtreme Gradient Boosting, are used to characterize changes in ADT based on network characteristics under a CAV scenario. The estimated models are validated and are found to yield acceptable performance. In addition, SHapley Additive exPlanations (SHAP) analysis tool is employed to investigate the impact of important features on changes in ADT. [ABSTRACT FROM AUTHOR]

Published
2021
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30. Analyzing the impact of neighborhood safety on active school travels.

Author

Shamshiripour, Ali, Shabanpour, Ramin, Golshani, Nima, Mohammadian, Abolfazl (Kouros), and Shamshiripour, Parisa

Subjects
*COLD weather conditions, *CHILDHOOD obesity, *SCHOOL safety, *TRAVEL hygiene, *SEVERE storms, *EXTREME value theory, *WEATHER, *NEIGHBORHOODS
Abstract

Childhood obesity has become a serious public health challenge during the past few decades, calling for policies to incorporate physical activity into students' routines. This study is an effort to contribute to the current literature of school travels by analyzing how improving safety of different neighborhoods in Chicago, Illinois would encourage students toward shifting to active modes, and how this interrelationship is affected by the severe weather conditions during the cold winters of the region. The results are complemented by multiple sensitivity analyses to quantify how these shifts would help different students burn extra walking calories (i.e. the extra calories each student burns due to walking more). We estimate multiple discrete continuous extreme value models to understand how flexible a student is in combining his/her most preferred transportation mode with other choices. Various sources of inter-personal heterogeneity are also captured by using a latent-classification framework as well as differentiating the before-school from after-school trip chains to consider the behavioral distinction, explicitly. Several explanatory variables are incorporated into the models, including socio-demographics of students and their household, land-use, crime prevalence, and seasonal/weather conditions. Per the results, improving safety of Chicago from its current condition to the national median, would encourage students to be up to 40% more active. This extra active travel demand would provide obese students aged 14–18 with 18% of the calorie burn they need to lose weight to the obesity cutoff and 13% of the calorie burn required for losing weight from the obesity cutoff to overweight. [ABSTRACT FROM AUTHOR]

Published
2020
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40. Analysis of evacuation destination and departure time choices for no-notice emergency events

Author

Golshani, Nima, Shabanpour, Ramin, Mohammadian, Abolfazl (Kouros), Auld, Joshua, and Ley, Hubert

Abstract

ABSTRACTOver the past decades, both the frequency and severity of disastrous events have increased all over the world. Thus far, many studies have focused on people’s evacuation behaviour during advance-notice events. However, little is known about their behaviour during no-notice events mainly because of the scarcity of available data. In an effort to address such a gap, this study presents a joint model to estimate evacuee’s destination and departure time choices in the context of no-notice emergency events. These two critical decisions can directly influence spatial and temporal traffic distributions in the network in case of disastrous events. A copula-based joint structure is proposed to explore the interdependencies between these evacuation attributes that stem from the shared factors influencing them and/or the causal effects that they might have on each other.

Published
2019
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41. Planning in-home activities in the ADAPTS activity-based model: a joint model of activity type and duration

Author

Shabanpour, Ramin, Golshani, Nima, Fasihozaman Langerudi, Mehran, and Mohammadian, Abolfazl (Kouros)

Abstract

ABSTRACTThe role of in-home activities in the process of planning and scheduling of individuals’ daily activities has been traditionally ignored because of two reasons: (i) in-home activities are not directly involved with trips; and (ii) scarcity of data sources that provide required details on planning and scheduling of these activities. However, considering the interchangeable nature of out-of-home and in-home activities, and the significant effects that they have on each other, we argue that failing to incorporate in-home activities may result in overestimating frequency and duration of out-of-home activities, which may lead to inconsistent and unrealistic activity schedules. Recently, we have upgraded the ADAPTS activity-based model to account for planning and scheduling of both in-home and out-of-home activities. This paper aims to enhance the in-home activity planning module by modelling the type and duration of the in-home activities in a joint structure. To achieve this goal, using the American Time Use Survey data, we estimate joint discrete-continuous models, in which activity type and activity duration are estimated by a multinomial logit and a log-linear regression model, respectively. The joint structure of these two models is established using copula approach that captures the unobserved shared factors affecting the two activity attributes. The results indicate that the estimated joint models significantly outperform the independent models in terms of goodness-of-fit and prediction accuracy.

Published
2018
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