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1. Gender split and safety behavior of cyclists and e-scooter users in Asbury Park, NJ

   https://doi.org/10.1016/j.cstp.2023.101073  

   Younes, Hannah; Noland, Robert B; Andrews, Clinton J (December 2023, Case Studies on Transport Policy)

   Micromobility usage has increased significantly in the last several years as exemplified by shared escooters and privately owned bicycles. In this study, we use traffic camera footage to observe the behavior of over 700 shared e-scooters and privately owned bicycles in Asbury Park, New Jersey. We address the following questions: (1) What are the behavioral differences between bicycle and e-scooter usage in terms of helmet use, bike lane / sidewalk use, gender split, group riding, and by time of day? (2) Are more protective conditions associated with helmet use and bike lane / sidewalk use? And (3) what is the gender split between e-scooter users and cyclists? We find notable differences in safety precautions: around one third of cyclists but no shared e-scooter users were observed wearing a helmet. Among cyclists, helmet use was more prominent among men than women. However, men were more likely to ride on the road than women. We also found that the gender split was narrower among e-scooter users, with a nearly even gender split – as opposed to cyclists, where only 21% of cyclists were observed to be women. Our findings suggest that e-scooter users take fewer safety precautions, in that they are less likely to use a bike lane and to wear a helmet. We conclude with policy implications with regards to safety and gender differences between these two modes. 

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2. Estimating road traffic impacts of commute mode shifts

   https://doi.org/10.1371/journal.pone.0279738  

   Hu, Yue; Barbour, William; Qian, Kun; Claudel, Christian; Samaranayake, Samitha; Work, Daniel B. (January 2023, PLOS ONE)

   Jin, Sheng (Ed.)

   This work considers the sensitivity of commute travel times in US metro areas due to potential changes in commute patterns, for example caused by events such as pandemics. Permanent shifts away from transit and carpooling can add vehicles to congested road networks, increasing travel times. Growth in the number of workers who avoid commuting and work from home instead can offset travel time increases. To estimate these potential impacts, 6-9 years of American Community Survey commute data for 118 metropolitan statistical areas are investigated. For 74 of the metro areas, the average commute travel time is shown to be explainable using only the number of passenger vehicles used for commuting. A universal Bureau of Public Roads model characterizes the sensitivity of each metro area with respect to additional vehicles. The resulting models are then used to determine the change in average travel time for each metro area in scenarios when 25% or 50% of transit and carpool users switch to single occupancy vehicles. Under a 25% mode shift, areas such as San Francisco and New York that are already congested and have high transit ridership may experience round trip travel time increases of 12 minutes (New York) to 20 minutes (San Francisco), costing individual commuters $1065 and $1601 annually in lost time. The travel time increases and corresponding costs can be avoided with an increase in working from home. The main contribution of this work is to provide a model to quantify the potential increase in commute travel times under various behavior changes, that can aid policy making for more efficient commuting. 

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3. Task-optimized User Clustering based on Mobile App Usage for Cold-start Recommendations

   Liu, Bulou; Bai, Bing; Xie, Weibang; Guo, Yiwen; Chen, Hao (August 2022, ACM SIGKDD Conference on Knowledge Discovery and Data Mining)

   This paper reports our recent practice of recommending articles to cold-start users at Tencent. Transferring knowledge from information-rich domains to help user modeling is an effective way to address the user-side cold-start problem. Our previous work demonstrated that general-purpose user embeddings based on mobile app usage helped article recommendations. However, high-dimensional embeddings are cumbersome for online usage, thus limiting the adoption. On the other hand, user clustering, which partitions users into several groups, can provide a lightweight, online-friendly, and explainable way to help recommendations. Effective user clustering for article recommendations based on mobile app usage faces unique challenges, including (1) the gap between an active user’s behavior of mobile app usage and article reading, and (2) the gap between mobile app usage patterns of active and cold-start users. To address the challenges, we propose a tailored Dual Alignment User Clustering (DAUC) model, which applies a sample-wise contrastive alignment to liminate the gap between active users’ mobile app usage and article reading behavior, and a distribution-wise adversarial alignment to eliminate the gap between active users’ and cold-start users’ app usage behavior. With DAUC, cold-start recommendation-optimized user clustering based on mobile app usage can be achieved. On top of the user clusters, we further build candidate generation strategies, real-time features, and corresponding ranking models without much engineering difficulty. Both online and offline experiments demonstrate the effectiveness of our work. 

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4. Plan Your System and Price for Free: Fast Algorithms for Multimodal Transit Operations

   https://doi.org/10.1287/trsc.2022.0452  

   Banerjee, Siddhartha; Hssaine, Chamsi; Luo, Qi; Samaranayake, Samitha (January 2025, Transportation Science)

   We study the problem of jointly pricing and designing a smart transit system, where a transit agency (the platform) controls a fleet of demand-responsive vehicles (cars) and a fixed line service (buses). The platform offers commuters a menu of options (modes) to travel between origin and destination (e.g., direct car trip, a bus ride, or a combination of the two), and commuters make a utility-maximizing choice within this menu, given the price of each mode. The goal of the platform is to determine an optimal set of modes to display to commuters, prices for these modes, and the design of the transit network in order to maximize the social welfare of the system. In this work, we tackle the commuter choice aspect of this problem, traditionally approached via computationally intensive bilevel programming techniques. In particular, we develop a framework that efficiently decouples the pricing and network design problem: Given an efficient (approximation) algorithm for centralized network design without prices, there exists an efficient (approximation) algorithm for decentralized network design with prices and commuter choice. We demonstrate the practicality of our framework via extensive numerical experiments on a real-world data set. We moreover explore the dependence of metrics such as welfare, revenue, and mode usage on (i) transfer costs and (ii) cost of contracting with on-demand service providers and exhibit the welfare gains of a fully integrated mobility system. Funding: This work was supported by the National Science Foundation [Awards CMMI-2308750, CNS-1952011, and CMMI-2144127]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0452 . 

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5. Exploring the role of ride-hailing in trip chains

   https://doi.org/10.1007/s11116-022-10269-w  

   Ahmed, Tanjeeb; Hyland, Michael (March 2022, Transportation)

   Abstract Ride-hailing can potentially provide a variety of benefits to individuals who need to chain several activities together within a single trip chain, relative to other travel modes. Using household travel diary/survey data, the goal of this study is to assess the role ride-hailing currently plays within trip chains. Specifically, the study aims to determine, within trip chains, who uses ride-hailing services, for what trip/activity purposes, and to/from what types of areas, as well as the characteristics of trip chains that involve ride-hailing segments. To meet these objectives, the study estimates a binary logit model using 2017 National Household Travel Survey data, where the dependent variable denotes the inclusion of at least one ride-hailing trip within a trip chain. Similar to the non-trip-chaining ride-hailing literature, this study indicates that trip chains with ride-hailing legs are positively associated with travelers who are younger, live in high-income households, frequently use transit, and reside in high-density areas. However, this study includes novel findings indicating statistically significant relationships between ride-hailing and trip chains that end in healthcare and social/recreational activities. Moreover, trip chains with ride-hailing tend to have fewer stops and longer activity durations than trip chains without ride-hailing. This study also includes nested logit choice models, wherein the dependent variable denotes the primary mode (ride-hailing, transit, personal vehicle, or non-motorized transport) of a trip chain. These model results provide additional insights into the role of ride-hailing within trip chains, as they allow for cross-mode comparisons. The paper discusses the potential transportation planning and policy implications of the model results as well as future research directions. 

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