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Greenhouse gas (GHG) emission and excessive fuel consumption have become a pressing issue nowadays. Particularly, CO2 emissions from transportation account for approximately one-quarter of global emissions since 2016. Electric vehicle (EV) is considered an appealing option to address the aforementioned concerns. However, with the growing EV market, issues such as insufficient charging infrastructure to support such ever-increasing demand emerge as well. Effectively forecasting the commercial EV charging demand ensures the reliability and robustness of grid utility in the short term and helps with investment planning and resource allocation for charging infrastructures in the long run. To this end, this article presents a time-series forecasting of the monthly commercial EV charging demand using a deep learning approach-Sequence to Sequence (Seq2Seq). The proposed model is validated by real-world datasets from the State of Utah and the City of Los Angeles. Two prediction targets, namely one-step ahead prediction and multi-step ahead prediction, are tested. Further, the model is benchmarked and compared against other time series and machine learning models. Experiments show that both Seq2seq and long short-term memory (LSTM) generate satisfactory prediction performance for one-step prediction. However, when performing the multi-step prediction, Seq2Seq significantly outperforms other models in terms of various performance metrics, indicating the model's strong capability for sequential data predictions. [ABSTRACT FROM AUTHOR]

This study presents a methodology for creating a synthetic travel demand, encompassing households and individuals and their daily activities, to support agent-based modeling (ABM) in urban planning and travel analysis. Unlike previous studies, which often rely on proprietary data, our approach is entirely based on open data, ensuring replicability by the broader research community. The research is among the first to propose the entire framework for travel demand synthesis and ABM. Results are validated against ground truth from the Census and other public data sources. The ABM results are compared to an Information Minimization (IM) model, which is an aggregated model capturing commuting patterns by race. The study contributes to the field by offering a comprehensive and replicable data foundation for ABM, serving as a valuable resource for evaluating population and travel demand synthesis algorithms. • We employed publicly available data to develop and validate ABM in transportation. • The study fosters the standardization of ABM model and increases its accessibility. • The research provides a complete and thorough examination of the entire ABM process. • The study does not rely on existing activity-based model and enables reproduction. [ABSTRACT FROM AUTHOR]

• We jointly optimize the scheduling and capacity of mixed human-driven and autonomous buses. • An integer nonlinear programming model is developed to consider both bus operating cost and passenger cost. • The model can be solved efficiently using a dynamic programming approach. • Simulation and empirical tests prove that autonomous buses are beneficial to both operators and passengers. It is a common practice for transit lines with fluctuating passenger demands to use demand-driven bus scheduling to reduce passenger waiting time and avoid bus overcrowding. However, current literature on the demand-driven bus scheduling generally assumes fixed bus capacity and exclusively optimizes bus dispatch headways. With the advent of connected and autonomous vehicle technology and the introduction of autonomous minibus/shuttle, the joint design of bus capacity and dispatch headway holds promises to further improving the system efficiency while reducing operating and passenger costs. This paper formulates this problem as an integer nonlinear programming model for transit systems operating with mixed human-driven and autonomous buses. In such mixed operating environment, the model simultaneously considers: (1) dynamic capacity design of autonomous bus, i.e., autonomous buses with varying capacity can be obtained by assembling and/or dissembling multiple autonomous minibuses; (2) trajectory control of autonomous bus, i.e., autonomous bus can dynamically adjust its running time as a function of its forward and backward headways; and (3) stop-level passenger boarding and alighting behavior. The objective of the model is designed to balance the trade-off between the operating costs of dispatching different types of bus and the costs of increased passenger waiting time due to inadequate bus dispatching. The model is solved using a dynamic programming approach. We show that the proposed model is effective in reducing passenger waiting time and total operating cost. Sensitivity analysis is further conducted to explore the impact of miscellaneous factors on optimal dispatching decisions, such as penetration rate of autonomous bus, bus running time variation, and passenger demand level. [ABSTRACT FROM AUTHOR]

• A dynamic control point selection algorithm for bus holding strategy is proposed. • A rolling-horizon performance model predicts bus operational states. • A cooperative game model with Myerson value ranks control points. • Passenger waiting time and headway variation are reduced by simulation tests. Bus holding is a widely used control method to regularize bus headways and reduce bus bunching. The method works in such a way by delaying buses at control points if their departure times or headways deviate from the planned ones. However, it may result in reduced bus commercial speeds and increased passenger onboard travel time. To avoid this problem, researchers have suggested that control points be spaced cautiously along the route such that only a few are needed. This study proposes a predictive headway-based bus holding strategy with dynamic control point importance ranking and selection based on the cooperative game theory. The framework considers not only individual control points' impact but also the collective group control effects. Specifically, the proposed framework consists of two components: a performance model and a cooperative game model. The performance model predicts headway performances of all running buses when different control point combinations are in effect. Dynamic bus running times and passenger demands are reflected in the model. Then, these headway performances are passed to the cooperative game model with control points being players and improvements in headway performances compared with that under no holding control being the utility function. The game is solved by Myerson value, a concept that extends Shapley value used for the normal cooperative game and considers the cooperation structure and potential worth of coalitions. We use Myerson value to rank the importance of control points on regularizing headways, as it measures the average marginal utility contribution of a control point to all possible coalitions that exclude that point. We prove that Myerson value lies in the Ω-core of the game and thus satisfies allocation efficiency, individual and coalition rationality. The proposed framework is applied to target headway control and two-way-looking self-equalizing headway control. Simulation results show that the framework can significantly reduce passenger waiting time and bus headway variation. [ABSTRACT FROM AUTHOR]

Abstract An agent-based modeling for dynamic ridesharing in a multimodal network is proposed in this paper. The study aims to evaluate the performance of dynamic ridesharing system within a multimodal network and explore the competing mechanism between dynamic ridesharing and public transit, with the presence of managed lane facility. The modeling process simulates the interaction between travelers and the network, and applies a heuristic algorithm to model travelers' decision making process under uncertainty. The model is applicable to networks with varying demographics. Multiple scenarios based on the classic Sioux Falls network have been examined. The modeling results demonstrate that the effects of dynamic ridesharing on a network differ with traffic demand and market penetrations of various travel modes. In networks with high travel demand and low market penetration of public transit, the benefits of dynamic ridesharing system on reducing congestion and providing reliable travel time are quite limited. To enhance the effectiveness of dynamic ridesharing, traffic operators may consider project investments on managed lane facilities. In networks with high market penetration of public transit, dynamic ridesharing may attract large amounts of short distance trips and aggravate congestion, especially at the initial launching phase. Policy makers would want to ensure that the existing infrastructure is sufficient to accommodate the extra traffic induced by ridesharing. Ridesharing service providers might also consider proper strategies to avoid "abuse" of the system by short trips and accelerate the market penetration. Highlights • We develop an agent-based model to explore the impact of dynamic ridesharing within a multimodal network. • The competing mechanism between dynamic ridesharing and public transit, with the presence of managed lanes, is quantified. • The benefits of dynamic ridesharing under various travel modes market penetrations are assessed. • The results offer guidance to policy makers and service providers on proper strategies under various network settings. [ABSTRACT FROM AUTHOR]

A high‐dimensional clustering‐based sampling method for roadway asset condition inspection is proposed in this study. The method complements existing literature by selecting sample roadway segments that contain multiple types of assets (e.g., signage, shoulder work, pavement marking, etc.) for the accurate estimation of their respective level of maintenance (LOMs). This is consistent with the standard maintenance procedure as inspection activities are often conducted on roadway segment basis. The proposed method consists of three components: current condition estimation, similarity matrix construction, and stratification. Current condition estimation predicts assets' "current condition" by considering historical inspection records. Similarity matrix construction represents the core piece of the sampling framework, which employs locality‐sensitive hashing algorithm to define the similarity between segments. The stratification process is implemented with spectral clustering, which assigns segments into clusters based on the similarity matrix. The proposed method outperforms simple random sampling, which is widely used in practice, especially under the circumstances where LOM varies greatly across assets. The main highlight of the proposed method is the ability to select sample segments with multiple types of assets that are representative of their respective LOMs of the full inventory, which directly translates into an efficient maintenance activity management. The method is implemented using asset inspection records in the state of Utah from September, 2014 to March 2016. It represents a potentially useful tool for agencies to effectively conduct asset inspection and can be easily adopted for choosing samples containing multiple features. [ABSTRACT FROM AUTHOR]

The rapid expansion of bikeshare programs nationwide provides opportunities to gain insights on the optimal development of multimodal networks and bike-friendly environments. The profusion of trajectory-level data produced by bikeshare systems allows for information extraction on users' route preferences and, if modeled properly, will lead to a greater understanding of road characteristics that are appealing to bikeshare users. Leveraging Global Positioning System (GPS) data obtained from the GREENbike program, this study proposes a method to characterize roadways (e.g. collector, peripheral road, attractive road, and local road) on the basis of a variety of network centrality functions. The methodology is able to uncover the structure of the underlying transportation network and identify locations of critical bicycle infrastructures. A series of centrality measures, including degree, shortest-path betweenness, and random-walk betweenness centrality are implemented to determine the roadway classifications. Their suitability and usability for this purpose is then explored and discussed at length through a sensitivity analysis. The method can be applied to any bikeshare system that has access to trajectory-level (i.e. GPS, crowdsourcing) data for identifying road attributes that are appealing to bike users. Results can effectively guide future investment choices. [ABSTRACT FROM AUTHOR]

This study examines the impact of a wide range of roadway geometric features on the severity outcomes of crashes occurred on rural two-lane highways. We argue that crash data have a hierarchical structure which needs to be addressed in modeling procedure. Moreover, most of previous studies ignored the impact of geometric features on crash types when developing crash severity models. We hypothesis that geometric features are more likely to determine crash type, and crash type together with other occupant, environmental and vehicle characteristics determine crash severity outcome. This paper presents an application of multilevel models to successfully capture both hierarchical structure of crash data and indirect impact of geometric features on crash severity. Using data collected in Illinois from 2007 to 2009, multilevel ordered logit model is developed to quantify the impact of geometric features and environmental conditions on crash severity outcome. Analysis results revealed that there is a significant variation in severity outcomes of crashes occurred across segments which verifies the presence of hierarchical structure. Lower risk of severe crashes is found to be associated with the presence of 10-ft lane and/or narrow shoulders, lower roadside hazard rate, higher driveway density, longer barrier length, and shorter barrier offset. The developed multilevel model offers greater consistency with data generating mechanism and can be utilized to evaluate safety effects of geometric design improvement projects. [ABSTRACT FROM AUTHOR]

The social functions of urbanized areas are highly dependent on and supported by the convenient access to public transportation systems, particularly for the less privileged populations who have restrained auto ownership. To accurately evaluate the public transit accessibility, it is critical to capture the spatiotemporal variation of transit services. This can be achieved by measuring the shortest paths or minimum travel time between origin-destination (OD) pairs at each time-of-day (e.g. every minute). In recent years, General Transit Feed Specification (GTFS) data has been gaining popularity for between-station travel time estimation due to its interoperability in spatiotemporal analytics. Many software packages, such as ArcGIS, have developed toolbox to enable the travel time estimation with GTFS. They perform reasonably well in calculating travel time between OD pairs for a specific time-of-day (e.g. 8:00 AM), yet can become computational inefficient and unpractical with the increase of data dimensions (e.g. all times-of-day and large network). In this paper, we introduce a new algorithm that is computationally elegant and mathematically efficient to address this issue. An open-source toolbox written in C++ is developed to implement the algorithm. We implemented the algorithm on City of St. George’s transit network to showcase the accessibility analysis enabled by the toolbox. The experimental evidence shows significant reduction on computational time. The proposed algorithm and toolbox presented is easily transferable to other transit networks to allow transit agencies and researchers perform high resolution transit performance analysis. [ABSTRACT FROM AUTHOR]

Public Transit Accessibility (PTA) analysis helps transit agencies and planners identify areas in need of transit service improvements and prioritize transit investments. To evaluate the accessibility of existing transit services and identify access gaps, it is critical to accurately estimate travel times between transit stops, which change throughout the day due to transit schedule variations. Commonly used methods in PTA ignore such temporal fluctuation. Moreover, these methods are unable to elucidate the causes of poor PTA. To address these issues, we first implemented an algorithm to effectively compute travel times at multiple departure times throughout the day in order to enable spatiotemporal PTA analysis. A series of indicators that are intuitive to interpret were developed to determine the varying causes of poor PTA and identify areas with immediate needs for improvements. We showcase the analytical framework using a transit network in the State of Utah operated by the Utah Transit Authority. The analysis is based solely on publicly-available open datasets, which makes it generally adaptable to other transit networks. Results can assist transit agencies with identifying areas in need of service improvement and prioritizing future investments. [ABSTRACT FROM AUTHOR]

A systematic approach to quantify Incident-Induced Delay (IID) is proposed in this study. The paper complements existing literature by developing a data-driven method to dynamically determine the spatiotemporal extent of individual incidents. The information construction process can be further used to uncover a variety of features that are associated with any specific incidents for optimal freeway management. Additionally, this study contributes two particular highlights: secondary incident identification and K-Nearest Neighbor (KNN) pattern matching. Secondary incident identification, as a pre-processing for IID estimation, disentangles the convoluted influences of subsequent incidents. The proposed method uses KNN pattern matching, an essentially heuristic search process to separate the delay solely induced by incidents from the recurrent congestion. The proposed algorithm on IID quantification was implemented on Interstate 15 in the state of Utah using data obtained from 2013. Results and implications are presented. Hot spot analysis is conducted that can be potentially used for incident mitigation and to inform investment decisions. The proposed methodology is easily transferable to any traffic operation system that has access to sensor data at a corridor level. [ABSTRACT FROM AUTHOR]

This paper performs a novel data-driven approach to optimize electric vehicle (EV) public charging. We translate the study area into a directed graph by partitioning it into discrete grids. A modified geographical PageRank (MGPR) model is developed to estimate EV charging demand, built upon trip origin–destination (OD) and social dimension features, and validated against real-world charging data. The results are fed into the capacitated maximal coverage location problem (CMCLP) model to optimize the spatial layout of public charging stations by maximizing their utilization. It is shown that MGPR can effectively quantify the EV charging demand with satisfactory accuracy. Optimized EV charging stations based on the CMCLP model can remedy the spatial mismatch between the EV demand and the existing charging station allocations. The developed methodological framework is highly generalizable and can be extended to other regions for EV charging demand estimation and optimal charging infrastructure siting. [ABSTRACT FROM AUTHOR]

With a growing awareness of the importance of near-road air pollution and an increasing population of near-road pedestrians, it is imperative to “nowcast” near-road air quality conditions to the general public. This necessitates the building hourly predictive models that are both accurate and easy to use. This study demonstrates an approach to model the hourly near-road Black Carbon (BC) concentrations given on-road traffic information and current meteorological conditions using datasets from two urban sites in Seattle, Washington. The optimal set of prediction variables is determined with a Bayesian Model Averaging (BMA) method and three different model structures are further developed and compared by goodness-of-fit. An innovative approach is proposed to translate wind direction from numerical values to categorical variables with statistical significance. By modeling the autocorrelation within the BC time series using an AR(1) component, the model achieves a satisfactory prediction accuracy. The conditional heteroscedasticity and heavy-tailed distribution of the model residuals are successfully identified and modeled by the General Auto Regressive Conditional Heteroscedasticity (GARCH) model, which provides valuable insights to the interpretation of prediction results. The methodological procedure demonstrated in selecting and fine-tuning the model is computationally efficient and valuable for further implementation onto online platforms for near-road BC nowcasting. A comparison between the two sites also reveals the effectiveness of local freight regulation for mitigating the environmental impacts from a heavy truck fleet. [ABSTRACT FROM AUTHOR]

Shared mobility is an essential component of the larger sharing economy. Ride-hailing, bike-sharing, e-scooters, and other types of shared mobility continue to grow worldwide. Among these services is microtransit, a new transport mode that extends transit coverage within a region. Mobile devices enable microtransit services, aggregating riders and using real-time routing algorithms to group customers traveling in similar directions. Meanwhile, the newly emerged coronavirus, COVID-19, has radically reshaped the ridership behavior of all transit services, including microtransit. While existing research evaluates the performance of microtransit pilot programs before the pandemic, there is no information concerning the spatio-temporal pattern of microtransit activities under the impact of COVID-19. The purpose of this paper is to apply eigendecomposition and k -clique percolation methods to uncover the spatio-temporal patterns of microtransit trips. Further, we used these approaches to identify underlying communities using data from a pilot program in Salt Lake City, Utah. The resulting research offers insight into how COVID-19 altered travel behavior. Specifically, eigendecomposition delineated the homogeneity and heterogeneity of travel patterns across temporal dimensions. We identified first mile/last mile trips as a major source of variance in both pre- and post-COVID periods and that transit-dependent users prove to be inelastic despite the threat of COVID-19. The k-clique percolation method detected possible community formations and tracked how these communities evolved during the pandemic. In addition, we systematically analyzed overlapping communities and the network structure around shared nodes by using a clustering coefficient. The workflow developed in this research broadly is generalizable and valuable for understanding the unique spatio-temporal patterns of microtransit. The framework can also help transit agencies with performance evaluation, regional transport strategies, and optimal vehicle dispatching. [ABSTRACT FROM AUTHOR]

Abstract: The large-scale implementation of High-Speed Rail (HSR) network in China not only offers a new option for travelers’ mode choice, but also may influence, or even generate, the redistribution of demographic and economic activities. As has been observed over the past several years in other countries, the impact of HSR spans a wide range. However, few quantitative studies have been conducted to measure this impact. As a new attempt, this study uses accessibility analysis for quantifying the impact of China’s HSR network. Weighted average travel times and travel costs, contour measures, and potential accessibility are employed as indicators of accessibility at the macro or national level. Forty-nine major cities in the HSR network are used in the accessibility analysis. Accessibility quantification and spatial distribution analysis for the study cities are performed on a Geographical Information System (GIS) platform. Accessibilities associated with varying availabilities of HSR, conventional rail, and airline are estimated and compared. The selected indicators and computational methods are found effective in evaluating the accessibility impacts of HSR from different conceptualization strategies and perspectives. They also offer complementary information on accessibility capacity of the study cities created by the HSR network. [Copyright &y& Elsevier]

Traffic volume is a critical piece of information in many applications, such as transportation long-range planning and traffic operation analysis. Effectively capturing traffic volumes on a network scale is beneficial to Transportation Systems Management & Operations (TSM&O). Yet it is impractical to install sensors to cover a large road network. To address this issue, spatial prediction techniques are widely performed to estimate traffic volumes at sites without sensors. In retrospect, most relevant studies resort to machine learning methods and treat each prediction location independently during the training process, ignoring the potential spatial dependency among them. This paper presents an innovative spatial prediction method of hourly traffic volume on a network scale. To achieve this, we applied a state-of-the-art tree ensemble model - extreme gradient boosting tree (XGBoost) - to handle the large-scale features and hourly traffic volume samples, due to the model's powerful scalability. Moreover, spatial dependency among road segments is taken into account in the proposed model using graph theory. Specifically, we created a traffic network graph leveraging probe trajectory data, and implemented a graph-based approach - breadth first search (BFS) - to search neighboring sites in this graph for computing spatial dependency. The proposed spatial dependency feature is subsequently incorporated as a new feature fed into XGBoost. The proposed model is tested on the road network in the state of Utah. Numerical results not only indicate high computational efficiency of the proposed model, but also demonstrate significant improvement in prediction accuracy of hourly traffic volume comparing with the benchmarked models. • We developed a tree ensemble model to spatially predict hourly traffic volume. • We explored the spatial dependency among road segments. • We propose an innovative approach by leveraging large-scale trajectory data. • Results indicate our model will generalize to accurately predict traffic volumes. [ABSTRACT FROM AUTHOR]

• This study addresses the overstaying issue from the activity perspective. • We refine the GTWR by enabling an activity-based Bayesian inference module. • The stochastic travel behaviors are incorporated in the Bayesian inference module. • The interconnections between overstaying and activities are captured in GTWR. • The entire modeling framework is evaluated in a real-world case study. Poor charging etiquette of Plug-in Electric vehicle (PEV) drivers, such as unplugging other PEVs and overstaying after the PEV is fully charged, will create a service bottleneck to charging resources and even impede PEV penetration. To explore the underlying linkage between PEV drivers' interim activities and the behavior of overstaying, this study introduces an innovative framework that implements Geographically and Temporally Weighted Regression (GTWR) with a dedicated activity-based Bayesian inference module. Specifically, the stochasticity of PEV drivers' travel behaviors is well addressed in the Bayesian inference module for travel choice modeling during charging sessions. Subsequently, the GTWR model is constructed based on predicted travel choices and expected durations of activities to capture the spatiotemporal interconnections between overstaying and activity characteristics. The entire modeling framework is further applied to a case study in Salt Lake City, Utah, and demonstrates superior adaptability in reasoning the impacts of spatiotemporal factors without survey data. [ABSTRACT FROM AUTHOR]

Transportation equity is a substantial concern for planners. Segregation and exposure analysis provide a lens from which community stakeholders can better decipher transportation equity challenges. This paper aims to expand racial segregation analysis beyond residential places to a more holistic activity space, including commuter populations. We filled existing research gaps on validity by calibrating Information Maximization (IM) model and a distance decay function to estimate race-specific driving patterns iteratively. A unique index of intergroup exposure and potential for contact between residents, workers, and commuters is proposed to understand the varying exposures different racial groups have with each other. We further identified the most racially-segregated road segments, residential and workplace areas, and how they become segregated based on the commuters' information. Given that exposure is a precursor to contact, understanding race-specific driving patterns is vital to understanding more extensive social mobility and segregation processes and their consequences for transport equity. [ABSTRACT FROM AUTHOR]

As the market penetration of electric vehicles (EVs) increases, the surge of charging demand could potentially overload the power grid and disrupt infrastructure planning. Hence, an efficient deployment strategy of electrical vehicle supply equipment (EVSE) is much needed. This study attempts to address the EVSE problem from a microscopic perspective by formulating the problem in two steps: public charging demand simulation and charging station location optimization. Specifically, we apply agent-based modeling approach to produce high-resolution daily driving profiles within an urban-scale context using MATSim. Subsequently, we perform EV assignment based on socioeconomic attributes to determine EV adopters. Energy consumption model and public charging rule are specified for generating synthetic public charging demand and such demand is validated against real-world public charging records to guarantee the robustness of simulation results. In the second step, we apply a location approach – capacitated maximal coverage location problem (CMCLP) model – to reallocate existing charging stations with the objective of maximizing the coverage of total charging demands generated from the previous step under the budget and load capacity constraints. The entire framework is capable of modeling the spatiotemporal distribution of public charging demand in a bottom-up fashion, and provide practical support for future public EVSE installation. • We applied agent-based modeling to produce high-resolution daily driving profiles. • We utilized a location approach to reallocate existing charging stations. • The synthetic charging demand is validated against real-world charging records. • The results can provide practical guidance for future charging station deployment. [ABSTRACT FROM AUTHOR]

Many police departments are meeting calls for transparency by releasing publicly accessible data. High-quality address locations are critical for successful and accurate geocoding, though the content and quality of that data can drastically vary across datasets. In this study, we showcase a two-step geocoding process that helps convert low-quality address locations into geo-locatable addresses using traditional geocoding and Jaro-Winkler edit distance methods with police stop data from the San Diego Police Department. For reference, only 83% of stops were geocoded when using traditional geocoding methods. By employing the Jaro-Winkler edit distance to clean the stop address strings, we were able to geocode 99% of stops. We further discuss data creation practices and solutions for data quality-related issues for police departments and researchers when using publicly available policing data. [ABSTRACT FROM AUTHOR]

High Occupancy Toll (HOT) lane systems are considered one of most effective countermeasures to mitigate freeway congestion. Existing studies have largely focused on developing optimal tolling strategies to maximize the benefits of congestion pricing. Limited effort has been made to model the dynamic feedback mechanism of drivers’ responses to tolling. A thorough understanding of how the interactive relationship between demands (in both HOT lane and general purpose lanes) and toll rates evolves over time is necessary. The underlying mechanism can be used directly for guiding future HOT facilities investment decisions. This study builds upon the traditional vector autoregressive model and enables its parameters to be time-varying. Such a relaxation, namely, time-varying parameter vector autoregressive model (TVP-VAR), is used to answer the following two questions: (1) Is there a time varying effect between general purpose lane volume, HOT lane volume and dynamic toll rate? (2) If there is, how to quantify such time-varying interdependencies? Based on the empirical data from loop detectors and toll logs on Washington State Route 167 (SR167), we identified the existence of time-varying effects between drivers’ responses and toll rates, and quantified the evolving interactions amongst HOT demand, general purpose demand and tolling via time-varying impulse responses. In addition, we found that drivers’ perceptions on HOT lanes across distinct geographical locations are significantly different. [ABSTRACT FROM AUTHOR]

There is a high potential of severe injury outcomes in traffic crashes on rural interstate highways due to the significant amount of high speed traffic on these corridors. Hierarchical Bayesian models are capable of incorporating between-crash variance and within-crash correlations into traffic crash data analysis and are increasingly utilized in traffic crash severity analysis. This paper applies a hierarchical Bayesian logistic model to examine the significant factors at crash and vehicle/driver levels and their heterogeneous impacts on driver injury severity in rural interstate highway crashes. Analysis results indicate that the majority of the total variance is induced by the between-crash variance, showing the appropriateness of the utilized hierarchical modeling approach. Three crash-level variables and six vehicle/driver-level variables are found significant in predicting driver injury severities: road curve, maximum vehicle damage in a crash, number of vehicles in a crash, wet road surface, vehicle type, driver age, driver gender, driver seatbelt use and driver alcohol or drug involvement. Among these variables, road curve, functional and disabled vehicle damage in crash, single-vehicle crashes, female drivers, senior drivers, motorcycles and driver alcohol or drug involvement tend to increase the odds of drivers being incapably injured or killed in rural interstate crashes, while wet road surface, male drivers and driver seatbelt use are more likely to decrease the probability of severe driver injuries. The developed methodology and estimation results provide insightful understanding of the internal mechanism of rural interstate crashes and beneficial references for developing effective countermeasures for rural interstate crash prevention. [ABSTRACT FROM AUTHOR]

Microscopic traffic simulation is a well-established tool for the analysis of transportation systems, with a wide variety of applications in operations, safety, and planning. An essential component of traffic simulation is the car-following model, which defines how vehicles interact with each other and controls acceleration/deceleration to maintain a desired set of speeds and distances when constrained by a leading vehicle. Car-following models are governed by a set of parameters that define the car's following behavior and can accommodate a range of values to reproduce desired conditions. Typically, calibration of a simulation scenario is conducted to approach a set of target macroscopic traffic condition indicators, such as speed, travel time, or queue, yet it rarely considers the accuracy of individual vehicle behavior, in part due to lack of detailed field data. In this paper, Naturalistic driving study (NDS) data sets were used to extract driving behavior on freeway segments at the microscopic level and directly characterize parameters in car-following models. The data extraction process is described, and the parameter values are illustrated for the Wiedemann 99 model implemented in commerically available software. Results highlight similarities and differences of these parameter values observed in the field and those by default in the software, and simulation outcomes upon NDS guided adjustment were analyzed. The process introduced can be expanded to similar data sets and other complex traffic conditions and therefore produce more accurate simulation results not only for metrics at a macroscopic level, but also for individual vehicle trajectories that closely mimic real-world driving. [ABSTRACT FROM AUTHOR]

Bike trajectories generated by the dockless bike-sharing service provides a great opportunity to explore users' travel behavior within the shared mobility transportation ecosystem. This paper proposes a new concept, namely biking islands, defined as geographical areas of interest with a high concentration of bike usage. Leveraging high-resolution trajectory data, biking islands are identified via percolation theory because of its suitability in describing the formation of clusters and critical road segments that have significant influence on biking behavior in an urban context. We showcase our methodology using the bike trajectory dataset provided by a market-leading company and use Shanghai, China as the study area. Results reveal a hierarchical structure of biking islands. With the increase of threshold, the biking islands start to shrink and split into various smaller ones. Larger biking islands are usually located in the central urban area of Shanghai and the Huangpu River acts as a natural barrier that impedes biking continuity across the region. Besides, the formation of biking islands is highly influenced by the surrounding land uses. The proposed concept and methods are not only helpful to understand travel behavior of cyclists and urban structures used for cycling, but also has the potential to support relevant urban and transportation planning, such as identifying designated non-motorized areas for cycling and biking facilities and pinpointing critical road segments that can improve the cycling efficiency of the entire network. Biking islands could be designed as designated areas for cycling where sufficient bike facilities are provided, and/or motorized transport modes are restricted or even prohibited, so as to ensure the convenience and safety of cyclists and support the development of bike-friendly cities. [ABSTRACT FROM AUTHOR]

This study analyzes driver injury severities for single-vehicle crashes occurring in rural and urban areas using data collected in New Mexico from 2010 to 2011. Nested logit models and mixed logit models are developed in order to account for the correlation between severity categories ( No injury , Possible injury , Visible injury , Incapacitating injury and fatality ) and individual heterogeneity among drivers. Various factors, such as crash and environment characteristics, geometric features, and driver behavior are examined in this study. Nested logit model and mixed logit model reveal similar results in terms of identifying contributing factors for driver injury severities. In the analysis of urban crashes, only the nested logit model is presented since no random parameter is found in the mixed logit model. The results indicate that significant differences exist between factors contributing to driver injury severity in single-vehicle crashes in rural and urban areas. There are 5 variables found only significant in the rural model and six significant variables identified only in the urban crash model. These findings can help transportation agencies develop effective policies or appropriate strategies to reduce injury severity resulting from single-vehicle crashes. [ABSTRACT FROM AUTHOR]

In light of the growing concerns of global climate change, the pace of transportation electrification has greatly accelerated in recent years as an effort towards net-zero greenhouse gas (GHG) emissions. However, it remains unclear how to effectively deploy and operate public charging infrastructure to best serve an electrified transportation system within a multi-modal context while maximizing the benefits of decarbonization. This is especially true when considering the GHG emitted by generating one kWh of electricity, i.e. the electricity carbon intensity, varies across a day due the change of generation mix between renewable and fossil fueled resources. To address this question, we propose a mechanism of shared charging hubs that can provide holistic energy management for both electric buses (EBs) and passenger electric vehicles (EVs). The deployment and operation of shared charging hubs is determined by a new spatio-temporal optimization model which aims to minimize GHG emission given a budget limit while avoiding the occurrence of massive spikes in peak power demand. This is achieved by coherently accommodating the charging demand of EBs and EVs, and explicitly integrating the time-varying electricity carbon intensity and vehicle-to-grid (V2G) technology. To demonstrate its effectiveness, the model is applied to the bus fleets operated by seven transit agencies and the park-and-ride facilities (for EVs) near twelve rail transit stations in Contra Costa County, California, USA. The results show that the shared charging hubs can lead to significant GHG emission reduction while mitigating the peak electricity demand. This research will help policymakers and transportation agencies make more informed decisions regarding the planning and design of charging infrastructure. • A scheme of shared charging hubs is proposed to decarbonize a regional transportation system. • The shared charging hubs are more cost-effective compared to charging stations dedicated to a single transportation mode. • The vehicle-to-grid technology can contribute substantially to decarbonization. [ABSTRACT FROM AUTHOR]

Crashes occurring on rural two-lane highways are more likely to result in severe driver incapacitating injuries and fatalities. In this study, mixed logit models are developed to analyze driver injury severities in single-vehicle (SV) and multi-vehicle (MV) crashes on rural two-lane highways in New Mexico from 2010 to 2011. A series of significant contributing factors in terms of driver behavior, weather conditions, environmental characteristics, roadway geometric features and traffic compositions, are identified and their impacts on injury severities are quantified for these two types of crashes, respectively. Elasticity analyses and transferability tests were conducted to better understand the models’ specification and generality. The research findings indicate that there are significant differences in causal attributes determining driver injury severities between SV and MV crashes. For example, more severe driver injuries and fatalities can be observed in MV crashes when motorcycles or trucks are involved. Dark lighting conditions and dusty weather conditions are found to significantly increase MV crash injury severities. However, SV crashes demonstrate different characteristics influencing driver injury severities. For example, the probability of having severe injury outcomes is higher when vans are identified in SV crashes. Drivers’ overtaking actions will significantly increase SV crash injury severities. Although some common attributes, such as alcohol impaired driving, are significant in both SV and MV crash severity models, their effects on different injury outcomes vary substantially. This study provides a better understanding of similarities and differences in significant contributing factors and their impacts on driver injury severities between SV and MV crashes on rural two-lane highways. It is also helpful to develop cost-effective solutions or appropriate injury prevention strategies for rural SV and MV crashes. [ABSTRACT FROM AUTHOR]