Ali Shamshiripour

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Scholarly Contributions

Chapters

• Shamshiripour, A., Shabanpour, R., Golshani, N., Auld, J., & Mohammadian, A. (2020). A flexible activity scheduling conflict resolution framework. In Mapping the Travel Behavior Genome. Elsevier. doi:10.1016/b978-0-12-817340-4.00016-4
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  Activity scheduling is a core component of activity-based travel demand models which replicates how individuals/agents decide about the placement and sequence of their activities in the daily schedule. The efficiency and accuracy of this module have a non-trivial impact on the overall performance of the activity-based model. This study reports a recent enhancement in the scheduler of POLARIS activity-based model, to incorporate more behavioral aspects of individuals’ decision making toward their daily activities. Specifically, we have developed a new activity conflict resolution model that handles the situations in which newly generated activities overlap activities that are already planned and have been previously scheduled. The accuracy and robustness of this component are crucial in the sense that subtle errors in scheduling could cause downstream errors in the model that may lead to substantial amounts of aggregate error in the final results. The proposed model benefits from a two-level process that accounts for a range of decisions involved in resolving an activity scheduling conflict, as well as well-defined and efficient feedback linkages to make sure that the components are generating consistent results. We have developed a stand-alone, C++ implementation code in order to test the overall accuracy of the framework against observed conflict resolution data. The results of these tests show that the predictions differ by at most 6.5% from observed distributions.

Journals/Publications

• Chen, K., Shamshiripour, A., Seshadri, R., Hasnine, M. S., Yoo, L., Guan, J., Alho, A. R., Feldman, D., & Ben-Akiva, M. (2024). Potential short-to long-term impacts of on-demand urban air mobility on transportation demand in North America. Transportation Research Part A: Policy and Practice, 190, 104288.
• Guan, J., Chen, K., Mao, R., Shamshiripour, A., Zhang, X., Liang, C., & Ben-Akiva, M. (2024). The willingness to pay for the automated vehicle subscription: Insights from a car-oriented population in China. Transportation Research Part A: Policy and Practice, 188, 104188.
• Jing, P., Seshadri, R., Sakai, T., Shamshiripour, A., Alho, A. R., Lentzakis, A., & Ben-Akiva, M. E. (2024). Evaluating congestion pricing schemes using agent-based passenger and freight microsimulation. Transportation Research Part A: Policy and Practice, 186(Issue). doi:10.1016/j.tra.2024.104118
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  The distributional impacts of congestion pricing have been widely discussed in the literature and the evidence on this is mixed. Some studies find that pricing is regressive whereas others suggest that it can be progressive or neutral depending on the specific spatial characteristics of the urban region, existing activity and travel patterns, and the design of the pricing scheme. Moreover, the welfare and distributional impacts of pricing have largely been studied in the context of passenger travel whereas freight has received relatively less attention. In this paper, we examine the impacts of several congestion pricing schemes on both passenger transport and freight in an integrated manner using a large-scale microsimulator (SimMobility) that explicitly simulates the behavioral decisions of the entire population of individuals and business establishments, dynamic multimodal network performance, and their interactions. Through simulations of a prototypical North American city, we find that a distance-based pricing scheme yields larger welfare gains than an area-based scheme, although the gains are a modest fraction of toll revenues (around 30%). In the absence of revenue recycling or redistribution, distance-based and cordon-based schemes are found to be particularly regressive. On average, lower income individuals lose as a result of the scheme, whereas higher income individuals gain. A similar trend is observed in the context of shippers — small establishments having lower shipment values lose on average whereas larger establishments with higher shipment values gain. We perform a detailed spatial analysis of distributional outcomes, and examine the impacts on network performance, activity generation, mode and departure time choices, and logistics operations.
• Jing, P., Seshadri, R., Sakai, T., Shamshiripour, A., Alho, A. R., Lentzakis, A., & Ben-Akiva, M. E. (2024). Evaluating congestion pricing schemes using agent-based passenger and freight microsimulation. Transportation Research Part A: Policy and Practice, 186, 104118.
• Mousavi, M., Emrani, J., Teleha, J. C., Jiang, G., Johnson, B. D., Shamshiripour, A., & Fini, E. H. (2024). Health Risks of Asphalt Emission: State-of-the-Art Advances and Research Gaps. Journal of Hazardous Materials, 136048.
• Chauhan, R., Bhagat-Conway, M., Capasso da Silva, D., Salon, D., Shamshiripour, A., Rahimi, E., Khoeini, S., Mohammadian, A., Derrible, S., & Pendyala, R. (2021). A database of travel-related behaviors and attitudes before, during, and after COVID-19 in the United States. Scientific Data, 8(1). doi:10.1038/s41597-021-01020-8
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  The COVID-19 pandemic has impacted billions of people around the world. To capture some of these impacts in the United States, we are conducting a nationwide longitudinal survey collecting information about activity and travel-related behaviors and attitudes before, during, and after the COVID-19 pandemic. The survey questions cover a wide range of topics including commuting, daily travel, air travel, working from home, online learning, shopping, and risk perception, along with attitudinal, socioeconomic, and demographic information. The survey is deployed over multiple waves to the same respondents to monitor how behaviors and attitudes evolve over time. Version 1.0 of the survey contains 8,723 responses that are publicly available. This article details the methodology adopted for the collection, cleaning, and processing of the data. In addition, the data are weighted to be representative of national and regional demographics. This survey dataset can aid researchers, policymakers, businesses, and government agencies in understanding both the extent of behavioral shifts and the likelihood that changes in behaviors will persist after COVID-19.
• Salon, D., Conway, M., da Silva, D., Chauhan, R., Derrible, S., Mohammadian, A., Khoeini, S., Parker, N., Mirtich, L., Shamshiripour, A., Rahimi, E., & Pendyala, R. (2021). The potential stickiness of pandemic-induced behavior changes in the United States. Proceedings of the National Academy of Sciences of the United States of America, 118(27). doi:10.1073/pnas.2106499118
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  Human behavior is notoriously difficult to change, but a disruption of the magnitude of the COVID-19 pandemic has the potential to bring about long-term behavioral changes. During the pandemic, people have been forced to experience new ways of interacting, working, learning, shopping, traveling, and eating meals. A critical question going forward is how these experiences have actually changed preferences and habits in ways that might persist after the pandemic ends. Many observers have suggested theories about what the future will bring, but concrete evidence has been lacking. We present evidence on how much US adults expect their own postpandemic choices to differ from their prepandemic lifestyles in the areas of telecommuting, restaurant patronage, air travel, online shopping, transit use, car commuting, uptake of walking and biking, and home location. The analysis is based on a nationally representative survey dataset collected between July and October 2020. Key findings include that the “new normal” will feature a doubling of telecommuting, reduced air travel, and improved quality of life for some.
• da Silva, D., Khoeini, S., Salon, D., Conway, M., Chauhan, R., Pendyala, R., Shamshiripour, A., Rahimi, E., Magassy, T., Mohammadian, A., & Derrible, S. (2021). How are Attitudes Toward COVID-19 Associated with Traveler Behavior During the Pandemic?. Transport Findings, 2021(Issue). doi:10.32866/001c.24389
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  This article uses data from the first wave of the COVID Future Panel study to evaluate attitudes towards COVID-19 and their influence on traveler behaviors. An exploratory factor analysis identified two underlying constructs based on the measured attitudes, namely “Concern about Pandemic Response” and “COVID Health Concern.” A cluster analysis based on the factor scores yielded four groups with distinct attitudes. Those primarily concerned about the pandemic response traveled the most using private vehicles, while those equally concerned about the response to the pandemic and the health effects of COVID-19 were found to use personal bicycles and transit the most.
• Ermagun, A., Shamshiripour, A., & Stathopoulos, A. (2020). Performance analysis of crowd-shipping in urban and suburban areas. Transportation, 47(4). doi:10.1007/s11116-019-10033-7
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  Crowd logistics is a novel shipping concept where delivery operations are carried out by using existing resources, namely vehicle capacity and drivers from the crowd, thereby offering potential for economic, social, and environmental benefits. Despite the promise of this new logistics model, little is known about its actual functioning, performance, and impact. This paper presents a pioneering study of the performance of a real crowd-shipping system in the U.S. using empirical data from 2 years of operations. We contribute to the literature by: (1) defining performance metrics and developing models that account for the specificity of crowd-shipping systems by distinguishing the essential stages from bidding to acceptance and delivery of shipments, (2) identifying the significant covariates, including shipment features, built environment, and socio-demographic factors giving rise to different delivery performance outcomes, and (3) deriving sensitivity analysis to study the performance and implications of crowd-shipping in urban and suburban areas. The analysis is formalized as two-level nested logit models with nests representing bidding and delivery outcomes. The results show that not only does the delivery outcome performance vary significantly between urban and suburban areas, but the explanatory factors also vary significantly for the two contexts. Additionally, several factors have ambiguous impacts depending on the stage. Larger shipment size (versus strict deadlines) leads to increasing (decreasing) the likelihood of bids being placed, while having the opposite effect when it comes to the delivery phase. The findings highlight the need for developing different strategies to foster and improve the performance of this novel system depending on both the urban–suburban shipping context and the stage of delivery.
• Rahimi, E., Shamshiripour, A., Samimi, A., & Mohammadian, A. (2020). Investigating the injury severity of single-vehicle truck crashes in a developing country. Accident Analysis and Prevention, 137(Issue). doi:10.1016/j.aap.2020.105444
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  Trucking plays a vital role in economic development in every country, especially countries where it serves as the backbone of the economy. The fast growth of economy in Iran as a developing country has also been accompanied by an alarming situation in terms of fatalities in truck-involved crashes, among the drivers and passengers of the trucks as well as the other vehicles involved. Despite the sizable efforts to investigate the truck-involved crashes, very little is known about the safety of truck movements in developing countries, and about the single-truck crashes worldwide. Thus, this study aims to uncover significant factors associated with injury severities sustained by truck drivers in single-vehicle truck crashes in Iran. The explanatory factors tested in the models include the characteristics of drivers, vehicles, and roadways. A random threshold random parameters hierarchical ordered probit model is utilized to consider heterogeneity across observations. Several variables turned out to be significant in the model, including driver's education, advanced braking system deployment, presence of curves on roadways, and high speed-limit. Using those results, we propose safety countermeasures in three categories of 1) educational, 2) technological, and 3) road engineering to mitigate the severity of single-vehicle truck crashes.
• Rahimi, E., Shamshiripour, A., Shabanpour, R., Mohammadian, A., & Auld, J. (2020). Analysis of Transit Users’ Response Behavior in Case of Unplanned Service Disruptions. Transportation Research Record, 2674(3). doi:10.1177/0361198120911921
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  Public transit disruption is becoming more common across different transit services, and can have a destructive influence on the resiliency of the transportation system. Even though transit agencies have various strategies to mitigate the probability of failure in the transit system by conducting preventative actions, some disruptions cannot be avoided because of their either unpredictable or uncontrollable nature. Utilizing recently collected data of transit users in the Chicago Metropolitan Area, the current study aims to analyze how transit users respond to an unplanned service disruption and disclose the factors that affect their behavior. In this study, a random parameter multinomial logit model is employed to consider heterogeneity across observations as well as panel effects. The results of the analysis reveal that a wide range of factors including socio-demographic attributes, personal attitudes, trip-related information, and built environment are significant in passengers’ behavior in case of unplanned transit disruptions. Moreover, the effect of service recovery time on passengers is not the same among all types of disrupted services; rail users are more sensitive to the recovery time as compared with bus users. The findings of this study provide insights for transportation authorities to improve the transit service quality in relation to user satisfaction and transportation resilience. These insights help transit agencies to implement effective recovery strategies.
• Shamshiripour, A., Rahimi, E., Auld, J., & (Kouros) Mohammadian, A. (2020). Investigating the influence of latent lifestyles on productive travels: Insights into designing autonomous transit system. Transportation Research Part A: Policy and Practice, 141(Issue). doi:10.1016/j.tra.2020.10.001
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  As a special case of multitasking, travel-based multitasking typically refers to conducting a set of in-vehicle activities while traveling. Travel-based multitasking has an indisputable influence on offering a pleasant travel experience to transit users during their rides, given that they can use their travel time to perform desirable activities and gain benefits in various form. For instance, the in-activities could help the rider free up time from his/her schedule for the day (i.e., a worthwhile use of travel time). In this study, we investigate how the worthwhileness of a travel-based multitasking could be under the influence of: (1) the transit user's lifestyle, and (2) socio-demographics, and (3) the characteristics of the transit trip. Towards this, we conducted an intercept survey focusing on the transit trips in the Chicago metropolitan area and analyzed it using latent class modeling approach. Per the results, two classes of transit users could be identified: (1) worthwhileness seekers, productively travelers and (2) leisure seekers, occasional worthwhile travelers. The results also suggest travel time, waiting time and walking distance to the transit station, and the set of in-vehicle activities as significant predictors of worthwhile use of travel time. The findings provide insights to policymakers for improving public transit systems in the current form, as well as designing an autonomous mobility system as the future form of public transit.
• Shamshiripour, A., Rahimi, E., Shabanpour, R., & Mohammadian, A. (2020). Dynamics of travelers’ modality style in the presence of mobility-on-demand services. Transportation Research Part C: Emerging Technologies, 117(Issue). doi:10.1016/j.trc.2020.102668
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  Modality style –defined as a set of frequent travel modes characterizing the travelers’ habits, routines, and predispositions– is a key player in forming dynamics of travelers’ mode choice behavior. This study aims to uncover the dynamics of modal preferences while the Mobility-on-Demand (MoD) services operate in the market. Using the 2017 National Household Travel Survey data, a Multiple Discrete Continuous Extreme Value model is developed to analyze the dynamics of individuals’ modality style. This model enables us to take into consideration marginal rates of substitutions between different transportation modes. Variables of interest in this analysis include the frequency of use of mobility-on-demand (MoD) services as well as the frequency of walking, biking, transit, and auto trips over the course of a month. The results of this study offer city planners and policymakers an opportunity to better understand the factors underlying modality styles, and which priorities to focus on when designing a sustainable development plan for resident-centric Smart Cities. According to the results, age, work status, education, auto availability, and the built environments are among the significant contributors to the modality styles. The results also indicate that the extent of the substitution relationship between transit and MoD services is highly context dependent.
• Shamshiripour, A., Shabanpour, R., Golshani, N., Mohammadian, A., & Shamshiripour, P. (2020). Analyzing the impact of neighborhood safety on active school travels. International Journal of Sustainable Transportation, 14(10). doi:10.1080/15568318.2019.1628327
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  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.
• Shamshiripour, A., Shamshiripour, A., Rahimi, E., Rahimi, E., Shabanpour, R., Shabanpour, R., Mohammadian, A., & Mohammadian, A. (2020). How is COVID-19 reshaping activity-travel behavior? Evidence from a comprehensive survey in Chicago. Transportation Research Interdisciplinary Perspectives, 7(Issue). doi:10.1016/j.trip.2020.100216
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  The novel COVID-19 pandemic has caused upheaval around the world and has led to drastic changes in our daily routines. Long-established routines such as commuting to workplace and in-store shopping are being replaced by telecommuting and online shopping. Many of these shifts were already underway for a long time, but the pandemic has accelerated them remarkably. This research is an effort to investigate how and to what extent people's mobility-styles and habitual travel behaviors have changed during the COVID-19 pandemic and to explore whether these changes will persist afterward or will bounce back to the pre-pandemic situation. To do so, a stated preference-revealed preference (SP-RP) survey is designed and implemented in the Chicago metropolitan area. The survey incorporates a comprehensive set of questions associated with individuals' travel behaviors, habits, and perceptions before and during the pandemic, as well as their expectations about the future. Analysis of the collected data reveals significant changes in various aspects of people's travel behavior. We also provide several insights for policymakers to be able to proactively plan for more equitable, sustainable, and resilient cities.
• Rahimi, E., Shamshiripour, A., Shabanpour, R., Mohammadian, A., & Auld, J. (2019). Analysis of transit users’ waiting tolerance in response to unplanned service disruptions. Transportation Research Part D: Transport and Environment, 77(Issue). doi:10.1016/j.trd.2019.10.011
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  Public transit not only provides an affordable, efficient, and green service but also plays a critical role in the development of resilient transportation systems in urban areas. Transit disruption as a common incident in transit service operation can severely affect the resiliency of the transportation system as well as users’ satisfaction. While it is of great interest to transportation authorities to understand passengers’ decision behavior during unplanned transit disruptions in order to implement efficacious recovery strategies, still little is known about users’ behavior in case of such incidents. The scarcity of available data is a major underlying factor for this gap. Utilizing a recently collected data of transit users in the Chicago metropolitan area, the current study investigates transit users’ waiting tolerance during unplanned service disruptions and disclose the factors that affect their behavior. A set of interval-censored accelerated failure time models using different survival distributions are developed, compared, and the factors influencing the survival functions of the waiting tolerance are identified. The results of the analysis reveal that, for instance, having experience of using ridesharing services decreases users’ waiting tolerance during a disruption. Further, built-environment attributes (such as the density of pedestrian-oriented links and transit service frequency), availability of alternative modes, transit service type, user's attitudes, and trip characteristics turn to be significant in users’ decision behavior.
• Shamshiripour, A., & Samimi, A. (2019). Estimating a mixed-profile MDCEV: case of daily activity type and duration. Transportation Letters, 11(6). doi:10.1080/19427867.2017.1337266
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  Multiple Discrete Continuous Extreme Value (MDCEV) has become popular in the past years. Yet, the model suffers from an ‘empirical identification’ issue that is mainly due to inter-relations between two of its parameters, α and γ. This paper presents a hybrid optimization paradigm (named HELPME) to address this issue in a basic MDCEV formulation and take full advantage of the model by estimating a ‘mixed-profile.’ HELPME benefits from a coarse-to-fine search strategy, in which a customized Electromagnetism-like meta-heuristic precedes a gradient-based approach. The Atlanta Regional Travel Survey (2011) is used to empirically analyze performance of HELPME as well as significance of the accuracy gap between the mixed-profile, and α and γ profiles. As part of the results, it is observed that in-sample fit is significantly improved, percentage error of out-of-sample prediction is reduced up to 97% in a 90% confidence level, and bias of out-of-sample predictions are reduced up to 67%.
• Shamshiripour, A., Golshani, N., Shabanpour, R., & Mohammadian, A. (2019). Week-Long Mode Choice Behavior: Dynamic Random Effects Logit Model. Transportation Research Record, 2673(10). doi:10.1177/0361198119851746
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  Modeling travelers’ mode choice behavior is an important component of travel demand studies. In an effort to account for day-to-day dynamics of travelers’ mode choice behavior, the current study develops a dynamic random effects logit model to endogenously incorporate the mode chosen for a day into the utility function of the mode chosen for the following day. A static multinomial logit model is also estimated to examine the performance of the dynamic model. Per the results, the dynamic random effects model outperforms the static model in relation to predictive power. According to the accuracy indices, the dynamic random effects model offers the predictive power of 60.0% for members of car-deficient households, whereas the static model is limited to 43.1%. Also, comparison of F1-scores indicates that the predictive power of the dynamic random effects model with respect to active travels is 47.1% whereas that of the static model is as low as 15.0%. The results indicate a significant day-to-day dynamic behavior of transit users and active travelers. This pattern is found to be true in general, but not for members of car-deficient households, who are found more likely to choose the same mode for two successive days.
• Shabanpour, R., Golshani, N., Shamshiripour, A., & Mohammadian, A. (2018). Eliciting preferences for adoption of fully automated vehicles using best-worst analysis. Transportation Research Part C: Emerging Technologies, 93(Issue). doi:10.1016/j.trc.2018.06.014
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  Autonomous mobility is one of the rapidly evolving aspects of smart transportation which carries the potential of reshaping both demand and supply sides of transportation systems. While understanding public opinions about autonomous vehicles (AVs) is a compelling step towards their successful implementation, still little is known about to which extent people will embrace this new technology and how the vehicle features will affect their adoption decision. This study presents a new approach for modeling the adoption behavior of fully AVs using the profile-case best-worst scaling model. In this approach, an AV profile which is characterized in terms of the main vehicle attributes and their associated levels is presented to the decision maker and he/she is asked to select the most and the least attractive attributes. Further, a binary adoption question at the end of the choice task inquires if the respondent is willing to purchase the described AV. Utilizing this method, we can recognize the difference between the intrinsic impacts of the vehicle attributes and the impact of the attribute levels on the adoption decision. Results of the analysis indicate that people are much more sensitive to the purchase price and incentive policies such as taking liability away from the “driver” in case of accidents and provision of exclusive lanes for AVs compared to other factors such as fuel efficiency, safety, or environmental friendliness. Further, it is found that millennials with higher income, those who live in the downtown area, and the majority of people who have experienced an accident in the past have greater interests in adopting this technology.
• Shabanpour, R., Shamshiripour, A., & Mohammadian, A. (2018). Modeling adoption timing of autonomous vehicles: innovation diffusion approach. Transportation, 45(6). doi:10.1007/s11116-018-9947-7
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  Autonomous vehicles (AVs) are expected to act as an economically-disruptive transportation technology offering several benefits to the society and causing significant changes in travel behavior and network performance. However, one of the critical issues that policymakers are facing is the absence of a sound estimation of their market penetration. This study is an effort to quantify the effect of different drivers on the adoption timing of AVs. To this end, we develop an innovation diffusion model in which individuals’ propensities to adopt a new technology such as AVs takes influence from a desire to innovate and a need to imitate the rest of the society. It also captures various sources of inter-personal heterogeneity. We found that conditional on our assumptions regarding the changes in market price of AVs over time, their market penetration in our study region (Chicago metropolitan area) will eventually reach 71.3%. Further, model estimation results show that a wide range of socio-demographic factors, travel pattern indicators, technology awareness, and perceptions of AVs are influential in people’s AV adoption timing decision. For instance, frequent long-distance travelers are found to make the adoption decision more innovatively while those who have experienced an accident in their lifetime are found to be more influenced by word of mouth.

Presentations

• Pooria, C., Shamshiripour, A., & Mohammadi, A. (2025).

  Smart Demand-Responsive Transit: Truthful People-Centric Design to Remove Information Asymmetry and Improve Welfare

  . Transportation Research Board 105th Annual Meeting.
• Shamshiripour, A. (2024). A Multi-day Needs-based Model for Activity and Travel Demand Analysis. Transportation Research Board.

Others

• Currans, K. M., & Shamshiripour, A. (2025, August).

  Memo 1 Policy Brief: Evaluation Transportation Impacts of Development. 

  . White paper for Pima County, Pima Prospers Update. https://content.civicplus.com/api/assets/13e7c55f-349b-45ec-b8bf-3ee00eb331df
• Currans, K. M., & Shamshiripour, A. (2025, August).

  Memo 2 Policy Brief: Non-Infrastructure Transportation Demand Management

  . White paper for Pima County, Pima Prospers Update. https://content.civicplus.com/api/assets/13e7c55f-349b-45ec-b8bf-3ee00eb331df
• Shamshiripour, A., & Currans, K. M. (2025, August).

  Memo 3 Policy Brief: Using ABM to Evaluate Transportation and Land Use Projects

  . White paper for Pima County, Pima Prospers Update. https://content.civicplus.com/api/assets/13e7c55f-349b-45ec-b8bf-3ee00eb331df