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The research team has utilized privacy-protected mobile device location data, integrated with COVID-19 case data and census population data, to produce a COVID-19 impact analysis platform that can inform users about the effects of COVID-19 spread and government orders on mobility and social distancing. The platform is being updated daily, to continuously inform decision-makers about the impacts of COVID-19 on their communities, using an interactive analytical tool. The research team has processed anonymized mobile device location data to identify trips and produced a set of variables, including social distancing index, percentage of people staying at home, visits to work and non-work locations, out-of-town trips, and trip distance. The results are aggregated to county and state levels to protect privacy, and scaled to the entire population of each county and state. The research team is making their data and findings, which are updated daily and go back to January 1, 2020, for benchmarking, available to the public to help public officials make informed decisions. This paper presents a summary of the platform and describes the methodology used to process data and produce the platform metrics.

This study leverages big location-based service data collected from mobile devices in 2019 to conduct a pedestrian and bicyclist safety analysis. Statistical models are estimated for pedestrian and bicyclist crash frequency at Maryland intersections using location-based service data as risk exposure data. The analysis is performed by employing prominent frequency modeling methodologies, including Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial regression techniques. The findings indicate that inclusion of big location-based service exposure data in the analysis improves the performance of the models. Further, the results suggest that key contributing factors to pedestrian and bicyclist crashes at Maryland intersections include the following: (i) intersection design- and traffic-related attributes, such as the number of intersection legs, presence of a traffic signal, and average level of traffic stress rating, as well as safety risk exposure measures, such as the average daily pedestrian, bicyclist, and vehicle volumes at the intersection; (ii) travel-related attributes, including public transportation and nonmotorized mode shares within the intersection’s census block group; (iii) land use and built environment attributes, such as road network density, activity density, and extent of walkability within the census block group; (iv) socioeconomic and sociodemographic attributes, including the percentage of low-income workers, households with no vehicles, African American population, and senior population within the census block group. The findings of the study show how big location-based service exposure data can be utilized to identify pedestrian and bicyclist safety risks and guide data-driven, evidence-based policy decision-making to improve the safety of vulnerable road users.

Over the past a few decades, a large number of researchers have worked on embedding mathematical methods into the prediction of traffic conditions. However, a lot of previous work only considered the temporal and spatial covariates as static explanatory variables. Different from previous studies, this paper investigates the conditional probability of freeway travel speed based on its lag values. Such conditional probability can be regarded as an attribute for each road segment. Based on this attribute, this paper proposes a conditional cumulative distribution function (CCDF)-based travel speed prediction approach. A real-world case study is conducted to predict short-term freeway speeds. The proposed CCDF-based model is compared with the Neural Networks (NN) model and the Support Vector Regression (SVR) model. The proposed model is faster in model training. Moreover, it can significantly improve the prediction accuracy, with 7.11% accuracy increase, compared to traditional NN and SVR prediction approaches.

ABSTRACTInformation influences travel behavior a great deal. This paper applies and further develops an agent-based approach to modeling travel behavior under uncertainty and different information provision strategies. Artificially intelligent agents, with the capability of learning, information acquisition, searching, and decision, are constructed instead of the typical representative agents formulated by utility maximization theory. Meanwhile, the presumption of sequential behavior process is relaxed. Agents are flexible to adjust travel mode, departure time, and route in response to a stimulus. A traffic simulator is also integrated in order to simulate agents' travel experiences on a transportation network. The agent-based model is then integrated into a simulation-based optimization (SBO) framework to analyze the effects of different information provision strategies. It is found that providing real-time traffic information to agents does not always result in improved network traffic condition or higher network reliability. Thus, we employ SBO technique to identify the optimal information provision strategies to support policy/planning decision-making.

ABSTRACTTravel time variability in a network is an important measure of the transportation system performance and a major factor influencing the decision-making of system management such as congestion pricing. The congestion-pricing problem with reliability maximization as the goal of a transportation network is characterized by expensive-to-evaluate objective functions without closed forms. In this paper, an effective simulation-based optimization (SBO) method is utilized to solve the problem. The network reliability is measured by a weighted average link travel time coefficient of variation (CV) with link traffic flows as the weights. We employ DynusT to evaluate the system reliability as the objective function corresponding to different toll charges for a new toll road in Maryland. The results show that the two optimal toll charge strategies improve the network-wide reliability, reducing the weighted travel time CV by 9.60% and 1.16%, respectively, when compared to the baseline toll.

ABSTRACTUnder transportation network supply and demand uncertainty, travel time reliability emerges from non-linear interactions among numerous travellers who are driven by self-interest, learn and adapt in changing situations, and therefore can be modelled as an emergent network property. This paper proposes a novel theoretical framework for the study of complexity regarding travel time reliability on the basis of empirically derived individual decision rules, and develops an agent-based evolutionary model for travel time reliability analysis. Findings show that actual route choice behaviours related to network reliability are often non-optimal, and that these behaviours themselves are important determinants of travel time reliability under network supply and demand uncertainty. While many travellers search for alternative routes under uncertainty, few travellers actually change routes. Route choice rules employed by travellers can successfully improve travel time reliability in most tested uncertainty scenarios, but they do not necessarily reduce absolute travel time. While reducing demand uncertainty improves travel time and planning under uncertainty, the model provides a theoretically sound tool for analysing the impact of traveller information systems on individual travel behaviour and network performance. It can also serve as an empirically estimated router for microscopic traffic simulators or act as a behavioural assignment algorithm for activity-based travel demand models.