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• This study investigates transit riders' waiting tolerance in response to unplanned service disruptions. • An intercept SP-RP survey was designed and conducted in the Chicago metropolitan area to collect required information. • Interval-censored accelerated failure time models are developed to uncover the factors affecting the waiting tolerance. 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. [ABSTRACT FROM AUTHOR]

• SMOTH performs well on accident/non-accident highly imbalanced data. • Abrupt change in speed is an important sign of accident occurrence. • Feature engineering is an essential part of training Machine Learning models. • Performance of detection models is varying in different time intervals after accidents occurred. • Best performance of detection models is achieved five minutes after accidents occurred. Detecting accidents is of great importance since they often impose significant delay and inconvenience to road users. This study compares the performance of two popular machine learning models, Support Vector Machine (SVM) and Probabilistic Neural Network (PNN), to detect the occurrence of accidents on the Eisenhower expressway in Chicago. Accordingly, since the detection of accidents should be as rapid as possible, seven models are trained and tested for each machine learning technique, using traffic condition data from 1 to 7 min after the actual occurrence. The main sources of data used in this study consist of weather condition, accident, and loop detector data. Furthermore, to overcome the problem of imbalanced data (i.e., underrepresentation of accidents in the dataset), the Synthetic Minority Oversampling TEchnique (SMOTE) is used. The results show that although SVM achieves overall higher accuracy, PNN outperforms SVM regarding the Detection Rate (DR) (i.e., percentage of correct accident detections). In addition, while both models perform best at 5 min after the occurrence of accidents, models trained at 3 or 4 min after the occurrence of an accident detect accidents more rapidly while performing reasonably well. Lastly, a sensitivity analysis of PNN for Time-To-Detection (TTD) reveals that the speed difference between upstream and downstream of accidents location is particularly significant to detect the occurrence of accidents. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In travel demand modelling literature, minor attention has been given to modelling and specifically micro-simulating the in-home activities. One reason could be associated with the fact that in-home activities do not explicitly involve trips. Besides, data limitation could be another reason since most of the travel surveys are based on the household travel diary approach and do not encompass in-home activity information. Nonetheless, incorporating people's in-home activities into the activity-based travel demand frameworks and perceiving the associations between the activities people perform in home and out of home could enhance the activity generation, planning, and scheduling procedures. In an effort to analyze such associations, this study uses a sequential conditional probability approach for modelling activity type and duration decisions of in-home activities in conjunction with out-of-home ones. The paper first proposes an algorithm to incorporate in-home activities within the Agent-based Activity Planning and Travel Scheduling (ADAPTS) framework. Then, models regarding generation and planning of in-home activities are presented and described accordingly. The new framework is capable of modelling and simulating in-home activities alongside and in conjunction with out-of-home activities with a special focus on a large set of in-home activity types. [ABSTRACT FROM AUTHOR]

This study focuses on an important transport-related long-term effect of the COVID-19 pandemic in the United States: an increase in telecommuting. Analyzing a nationally representative panel survey of adults, we find that 40–50% of workers expect to telecommute at least a few times per month post-pandemic, up from 24% pre-COVID. If given the option, 90–95% of those who first telecommuted during the pandemic plan to continue the practice regularly. We also find that new telecommuters are demographically similar to pre-COVID telecommuters. Both pre- and post-COVID, higher educational attainment and income, together with certain job categories, largely determine whether workers have the option to telecommute. Despite growth in telecommuting, approximately half of workers expect to remain unable to telecommute and between 2/3 and 3/4 of workers expect their post-pandemic telecommuting patterns to be unchanged from their pre-COVID patterns. This limits the contribution telecommuting can make to reducing peak hour transport demand. [ABSTRACT FROM AUTHOR]

• We investigate the dynamics of modal preferences while the MoD services operate. • This study uses the 2017 National Household Travel Survey data. • We utilized the MDCEV formulation to consider marginal rates of substitution. • The substitution relationship between transit and MoD is highly context dependent. • A pro-active approach to planning towards Smart Cities will be vital. 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. [ABSTRACT FROM AUTHOR]

• This study investigated the severity of single-vehicle truck crashes occurred in Iran. • This study highlighted the contextual differences in the severity of single-vehicle truck crashes. • This study used a random thresholds random parameters hierarchical ordered probit (HOPIT) model. • According to the results, several safety countermeasures are proposed to mitigate the severity of single-vehicle truck crashes. 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. [ABSTRACT FROM AUTHOR]

• Develop an XGBoost model to detect accidents with detection rate of 79 %, and AUC of 89 %. • The developed model is robust and interpretable thanks to SHAP. • Complex interrelated impacts of selected features are captured and analyzed. Detecting traffic accidents as rapidly as possible is essential for traffic safety. In this study, we use eXtreme Gradient Boosting (XGBoost)—a Machine Learning (ML) technique—to detect the occurrence of accidents using a set of real time data comprised of traffic, network, demographic, land use, and weather features. The data used from the Chicago metropolitan expressways was collected between December 2016 and December 2017, and it includes 244 traffic accidents and 6073 non-accident cases. In addition, SHAP (SHapley Additive exPlanation) is employed to interpret the results and analyze the importance of individual features. The results show that XGBoost can detect accidents robustly with an accuracy, detection rate, and a false alarm rate of 99 %, 79 %, and 0.16 %, respectively. Several traffic related features, especially difference of speed between 5 min before and 5 min after an accident, are found to have relatively more impact on the occurrence of accidents. Furthermore, a feature dependency analysis is conducted for three pairs of features. First, average daily traffic and speed after accidents/non-accidents time at the upstream location are interpreted jointly. Then, distance to Central Business District and residential density are analyzed. Finally, speed after accidents/non-accidents time at upstream location and speed after accidents/non-accidents time at downstream location are evaluated with respect to the model's output. [ABSTRACT FROM AUTHOR]

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., Competing Interests: The authors do not have any conflicts of interest to declare., (© 2020 The Authors. Published by Elsevier Ltd.)