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People are more likely to choose to take a bus for travel theoretically if both the origin and destination are located near bus stops. However, many of these trips are now replaced by online car-hailing service in China. This study makes the first attempt to look into this special hailing behavior by conducting a contrast analysis between hailing trip and its corresponding bus journey with the same origin and destination. We proposed an identification approach to classify the special hailing trips into four types of hailing behaviors with various hailing willingness. Structural equation modeling is then used to explore the impacts of land use characteristics, features of transport facilities and hailing ridership characteristics on the performance of special hailing behavior. Based on the results, 10.86 percent of special hailing trips have origins and destinations that are located within 100 m distance of the bus stop. Imbalance travel demand for special hailing at different periods of the day indicates the time-varying characteristic, which reaches its highest level in the afternoon. Especially through the major mobility trends of these trips, the spatial patterns of special hailing behavior can be divided into two regimes (i.e. urban and suburban areas) corresponding to the agglomeration area of varied social activities and residential areas. The generalized travel costs of bus and online car-hailing reflect the hailing willingness of travelers to some extent. People face the tradeoff between the two travel modes and tend to choose instead to use online car-hailing in different situations, and the SEM results support this. Notably, the accessibility of bus stops and users' bus travel experience show significant negative effects on special hailing behavior, and the need to transfer during a bus journey can particularly influence a traveler's initiative to take a bus. The findings provide insights into the special online car-hailing usage patterns at bus stop level and its role in urban transport, which can support accurate planning and upgrading of sustainable public transit to increase competitiveness at the big picture. •The study explore the special online car-hailing behavior that both the origin and destination are located near bus stops. •This paper develops a more objective valuation method of generalized travel cost. •All the special hailing trips are divided into four types with various hailing willingness. •SEM method is used to examine the key factors behind this special hailing behavior. [ABSTRACT FROM AUTHOR]

Prearranged and on-demand ride services that match passengers and drivers using smartphone applications over a network have developed rapidly across the world. However, the maximum efficiency of the entire hailing demand market and the driver deserve further attention to identify potential improvements. This paper makes the first attempt to conduct a quantitative analysis of the intangible costs generated from ridesourcing trips using an observed ridesourcing dataset provided by Didi Chuxing, including the waiting time costs attributable to waiting for the passenger and the future opportunity costs of different Trip Order Markets. Then, a quantitative method is developed to calculate the specific value of the above intangible costs of each ridesourcing trip. The results show that the average cost of a trip drops by 9.64% when intangible costs are considered, and a large difference exists among the values under various spatial-temporal conditions. In addition, the relative relationship between two types of intangible costs shows a large discrepancy in some sensitive areas. The findings of this study can help transit agencies better understand ridesourcing service and develop strategies for setting reasonable prices and allocating capacity. • This paper explores the composition of trip cost of online car-hailing. • The definition of intangible costs are conducted, including waiting time cost and future opportunity cost. • A logical and feasible intangible costs quantitative method is proposed for the study. • The relative relationship among intangible costs also can be used on sensitive areas identification. [ABSTRACT FROM AUTHOR]

Abstract Recent advances in bottleneck studies have highlighted that different architectural adjustments at the exit may reduce the probability of clogging at the exit thereby enhancing the outflow of the individuals. However, those studies are mostly limited to the controlled experiments with non-human organisms or predictions from simulation models. Complementary data with human subjects to test the model's prediction is limited in literature. This study aims to examine the effect of different geometrical layouts at the exit towards the pedestrian flow via controlled laboratory experiments with human participants. The experimental setups involve pedestrian flow through 14 different geometrical configurations that include different exit locations and obstacles near exit under normal and slow running conditions. It was found that corner exit performed better than middle exit under same obstacle condition. Further, it was observed that the effectiveness of obstacle is sensitive to its size and distance from the exit. Thus, with careful architectural adjustment within a standard escape area, a substantial increase in outflow under normal and slow running conditions could be achieved. However, it was also observed that placing the obstacle too close to the exit can reduce outflow under both normal and slow running conditions. Moreover, we could not observe "faster-is-slower" effect under slow running condition and instead noticed "faster-is-faster" effect. In addition, the power law fitted headway distribution demonstrated that any architectural configurations that enhanced the outflow have higher exponent value compared to the other configuration that negates the outflow. The findings from this paper demonstrate that there is a scope to adjust the architectural elements to optimize the maximum outflow at the egress point. Further, the output from the experiments can be used to develop and verify mathematical models intended to simulate crowd evacuation. Highlights • Careful adjustments of structural features can increase the outflow of pedestrians. • Obstacles have different performance at middle and corner exits. • Column size and distance to the exit affect the outflow of pedestrians. • "Faster-is-faster" effect is observed under slow running condition. [ABSTRACT FROM AUTHOR]

Urban buses are heavy vehicles that move frequently throughout the day, and most of them are propelled by heavy-duty diesel engines. For these reasons, they have energy and environmental impacts that should not be ignored. Consequently, the primary objectives of this study were to compare the changes in bus speed, acceleration, and emissions between bus stops, intersections, and road sections by applying statistical methods; and to develop a vehicle specific power (VSP)-based artificial neural network (ANN) model to estimate emissions of CO, HC, NO X , and CO 2 for four different fuel types of buses including gas-electric hybrid electric buses (GEHE bus), compressed natural gas buses (CNG bus), EURO 4 heavy-duty diesel engine buses (EURO 4 bus), and EURO 5 heavy-duty diesel engine buses (EURO 5 bus). The results of t -tests (with p -values varying between <0.001 and 0.026, which were not >0.050) showed that the differences in emissions between different locations and between different fuel types of buses were all statistically significant. In addition, to evaluate the performance of the proposed method, a polynomial regression model using linear, quadratic, and cubic terms of transient speed and acceleration was utilized for comparison. According to the results, the proposed method had more accurate and reliable estimation, which increased the lower 10% of absolute percentage error (Lower-10% APE) by 65.2%; reduced mean absolute percentage error (MAPE) by 41.4%, root mean squared error (RMSE) by 44.9%, and mean absolute error (MAE) by 43.5%; and increased R-squared from 0.659 to 0.781. [ABSTRACT FROM AUTHOR]

Bicycle-sharing systems (BSSs) have become a prominent feature of the transportation network in many cities. Along with the boom of BSSs, cities face the challenge of bicycle unavailability and dock shortages. It is essential to conduct rebalancing operations, the success of which largely depend on users’ demand prediction. The objective of this study is to develop users’ demand prediction models based on the rental data, which will serve rebalancing operations. First, methods to collect and process the relevant data are presented. Bicycle usage patterns are then examined from both trip-based aspect and station-based aspect to provide some guidance for users’ demand prediction. After that, the methodology combining cluster analysis, a back-propagation neural network (BPNN), and comparative analysis is proposed to predict users’ demand. Cluster analysis is used to identify different service types of stations, the BPNN method is utilized to establish the demand prediction models for different service types of stations, and comparative analysis is employed to determine if the accuracy of the prediction models is improved by making a distinction among stations and working/nonworking days. Finally, a case study is conducted to evaluate the performance of the proposed methodology. Results indicate that making a distinction among stations and working/nonworking days when predicting users’ demand can improve the accuracy of prediction models. [ABSTRACT FROM AUTHOR]

Complex movement patterns of pedestrian traffic, ranging from unidirectional to multidirectional flows, are frequently observed in major public infrastructure such as transport hubs. These multidirectional movements can result in increased number of conflicts, thereby influencing the mobility and safety of pedestrian facilities. Therefore, empirical data collection on pedestrians’ complex movement has been on the rise in the past two decades. Although there are several reviews of mathematical simulation models for pedestrian traffic in the existing literature, a detailed review examining the challenges and opportunities on empirical studies on the pedestrians complex movements is limited in the literature. The overall aim of this study is to present a systematic review on the empirical data collection for uni- and multidirectional crowd complex movements. We first categorized the complex movements of pedestrian crowd into two general categories, namely, external governed movements and internal driven movements based on the interactions with the infrastructure and among pedestrians, respectively. Further, considering the hierarchy of movement complexity, we decomposed the externally governed movements of pedestrian traffic into several unique movement patterns including straight line, turning, egress and ingress, opposing, weaving, merging, diverging, and random flows. Analysis of the literature showed that empirical data were highly rich in straight line and egress flow while medium rich in turning, merging, weaving, and opposing flows, but poor in ingress, diverging, and random flows. We put emphasis on the need for the future global collaborative efforts on data sharing for the complex crowd movements. [ABSTRACT FROM AUTHOR]

This study proposes an improved model and algorithm for the large-scale multi-depot vehicle scheduling problem (MDVSP) with departure-duration restrictions. In this study, the time-space network is applied to model the large-scale MDVSP. Considering that crews usually change shifts in the depot, departure-duration restrictions are added to the classic set-partitioning model to ensure that buses return to the depot when crews reach their working time limits. By embedding a preliminary exploring tactic to the shortest path faster algorithm (SPFA), researchers developed an improved large neighborhood search (LNS) algorithm to solve large-scale instances of MDVSP with departure-duration restrictions. The proposed methodology is applied to a real-life case in China and several test instances. The results show that the improved LNS algorithm can achieve very good performance in computational efficiency without deteriorating solution quality, which is important for large-scale systems. More specifically, the total cost of the improved LNS algorithm is approximately equal to branch-and-price, but the computational time is much shorter in the case study. For test instances with different number of timetabled trips (500, 1000, 1500, and 2000), the Quality Gap (QG) is very small, approximately 0.35%, 0.38%, 0.63%, and 0.93%, while the Efficiency Ratio (ER) reaches up to 2.89, 2.98, 3.65, and 3.79, respectively. [ABSTRACT FROM AUTHOR]

The investigation of relationships between traffic crashes and relevant factors is important in traffic safety management. Various methods have been developed for modeling crash data. In real world scenarios, crash data often display the characteristics of over-dispersion. However, on occasions, some crash datasets have exhibited under-dispersion, especially in cases where the data are conditioned upon the mean. The commonly used models (such as the Poisson and the NB regression models) have associated limitations to cope with various degrees of dispersion. In light of this, a generalized event count (GEC) model, which can be generally used to handle over-, equi-, and under-dispersed data, is proposed in this study. This model was first applied to case studies using data from Toronto, characterized by over-dispersion, and then to crash data from railway-highway crossings in Korea, characterized with under-dispersion. The results from the GEC model were compared with those from the Negative binomial and the hyper-Poisson models. The cases studies show that the proposed model provides good performance for crash data characterized with over- and under-dispersion. Moreover, the proposed model simplifies the modeling process and the prediction of crash data. [ABSTRACT FROM AUTHOR]

A recent crowd stampede during a New Year's Eve celebration in Shanghai, China resulted in 36 fatalities and over 49 serious injuries. Many of such tragic crowd accidents around the world resulted from complex multi-direction crowd movement such as merging behavior. Although there are a few studies on merging crowd behavior, none of them have conducted a systematic analysis considering the impact of both merging angle and flow direction towards the safety of pedestrian crowd movement. In this study, a series of controlled laboratory experiments were conducted to examine the safety constraints of merging pedestrian crowd movements considering merging angle (60°, 90° and 180°) and flow direction under slow running and blocked vision condition. Then, macroscopic and microscopic properties of crowd dynamics are obtained and visualized through the analysis of pedestrian crowd trajectory data derived from video footage. It was found that merging angle had a significant influence on the fluctuations of pedestrian flows, which is important in a critical situation such as emergency evacuation. As the merging angle increased, mean velocity and mean flow at the measuring region in the exit corridors decreased, while mean density increased. A similar trend was observed for the number of weaving and overtaking conflicts, which resulted in the increase of mean headway. Further, flow direction had a significant impact on the outflow of the individuals while blocked vision had an influence on pedestrian crowd interactions and merging process. Finally, this paper discusses safety assessments on crowd merging behaviors along with some recommendations for future research. Findings from this study can assist in the development and validation of pedestrian crowd simulation models as well as organization and control of crowd events. [ABSTRACT FROM AUTHOR]

Real-time traffic flow forecasting is of great importance in the development of advanced traffic management systems and advanced traveler information systems. Traffic flow is evaluated using time series, and the Autoregressive Integrated Moving Average (ARIMA) model has been commonly used for determining the regression-type relationship between historical and future data. However, the performance of the ARIMA model is limited by the difficulty of capturing nonlinear patterns and the challenges of diagnosing permanent white noises. Hence, a hybrid method of ARIMA-EGARCH-M-GED was developed with the intent to address those limitations. It combines the linear ARIMA model with a nonlinear model of Exponent Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) to capture heteroscedasticity (the variance of random error varying across the data) of traffic flow series. EGARCH in Mean (EGARCH-M), which corrects the expression of conditional variance by connecting the conditional mean directly with the variance, was introduced to better restrain the influence of abnormal data. Moreover, the tail of the generalized error distribution (GED) is better than that of the normal distribution in characterizing the features of time series, especially heteroscedasticity of residual sequences. Data collected from an interstate highway (I-80 in California) with a sampling period of 5 minutes were used to evaluate the performance of the proposed model. The results from the hybrid model were compared with ARIMA, an artificial neural network, and a K-nearest neighbor model. The results showed that the hybrid model outperformed the other methods in terms of accuracy and reliability. Overall, the proposed model performed well in tracking the features of measured data and controlling the impact of abnormal data. [ABSTRACT FROM PUBLISHER]

A two-dimensional logistic model is proposed to describe the co-development of road networks and vehicle ownership. The endogenous interaction between road networks and vehicle ownership and how natural market forces and policies transformed into their co-development are considered jointly in this model. If the involved parameters satisfy a certain condition, the proposed model can arrive at a steady equilibrium level and the final development scale will be within the maximum capacity of an urban traffic system; otherwise, the co-development process will be unstable and even manifest chaotic behavior. Then sensitivity tests are developed to determine the proper values for a series of parameters in this model. Finally, a case study, using Beijing City as an example, is conducted to explore the applicability of the proposed model to the real condition. Results demonstrate that the proposed model can effectively simulate the co-development of road network and vehicle ownership for Beijing City. Furthermore, we can obtain that their development process will arrive at a stable equilibrium level in the years 2040 and 2045 respectively, and the equilibrium values are within the maximum capacity. [ABSTRACT FROM AUTHOR]

Ridesourcing or on-demand ridesharing, offers a sustainable mobility option that connects drivers with passengers via mobile application directly, which helps reduce unnecessary vehicle cruising and energy consumption. It plays a crucial role in urban mobility within the built environment. However, the interdependency between ridesourcing usage and built environment has not been addressed adequately, particularly in the critical regions that have significant influence on ridesourcing usage in an urban context. Based on percolation theory, this study suggests a new concept, namely ridesourcing usage islands, defined as geographical areas of interest with a high or low concentration of ridesourcing usage. Within these noteworthy areas, a machine learning method, gradient boosting decision trees (GBDT), is further innovatively adopted to investigate the refined and discontinuous non-linear impacts of built environment on ridesourcing usage. The results reveal a hierarchical structure of ridesourcing usage islands. Regional imbalances of travel supply and demand at usage island level are sporadically identified across several regions. Besides, the formation of usage islands is highly influenced by the surrounding built environment. Most importantly, employment density and residential density have joint contribution of almost 20% for ridesourcing pick up demand and drop off demand respectively, reflecting the role of ridesourcing in commuting. Regardless of island's type, built environment features show obvious threshold effects on ridesourcing usage, and their specific effective ranges are different from each other. Findings in this paper are expected to help better understand ridesourcing use as a function of urban built environment, and provide valuable inputs for ridesourcing management and sustainable urban development. [ABSTRACT FROM AUTHOR]

Abstract: The modeling of relationships between motor vehicle crashes and underlying factors has been investigated for more than three decades. Recently, many highway safety studies have documented the use of negative binomial (NB) regression models. On rare occasions, the Poisson model may be the only alternative especially when crash sample mean is low. Pearson's X 2 and the scaled deviance (G 2) are two common test statistics that have been proposed as measures of goodness-of-fit (GOF) for Poisson or NB models. Unfortunately, transportation safety analysts often deal with crash data that are characterized by low sample mean values. Under such conditions, the traditional test statistics may not perform very well. This study has three objectives. The first objective is to examine all the traditional test statistics and compare their performance for the GOF of accident models subjected to low sample means. Secondly, this study proposes a new test statistic that is not dependent on the sample size for Poisson regression model, as opposed to the grouped G 2 method. The proposed method is easy to use and does not require grouping data, which is time consuming and may not be feasible to use if the sample size is small. Moreover, the proposed method can be used for lower sample means than documented in previous studies. Thirdly, this study provides guidance on how and when to use appropriate test statistics for both Poisson and negative binomial (NB) regression models. [Copyright &y& Elsevier]

This paper synthesizes information obtained from a comprehensive literature review and agency surveys on the state of the practice of fixed automated spray technology (FAST) systems. Experience with FAST systems in North America and Europe has revealed a mixed picture. On the one hand, several studies have indicated reductions in mobile operation costs and significant reductions in crash frequency, resulting in favorable benefit-cost ratios. On the other hand, there have been a variety of problems related to activation frequency, system maintenance and training. Installing a FAST system is complex and the challenges are often site specific. However, the evaluations cited show that FAST systems can be cost-beneficial if their locations are carefully chosen and if the system is supported with reliable environmental sensors. Finally, it was found that application of FAST systems in North America have recently increased. [ABSTRACT FROM AUTHOR]

Abstract: Gateway Monuments are free standing roadside structures or signage that communicate the name of a city, country or township to motorists. The placement of such monuments within state-controlled right-of-way is a relatively recent occurrence in California. As a result, the California Department of Transportation (Caltrans) initiated research to quantify the impacts that this type of signage may or may not have on crashes in their vicinity. To date, no specific research has examined the impact such features have on crashes. To determine whether these features impacted safety, the before–after study method using the Empirical Bayes technique was used, with reference groups and Safety Performance Functions adapted from existing studies, eliminating the need to calibrate new models. Results indicated that, on an individual basis, no deterioration in safety was observed at any monument site. When all sites were examined collectively (using two different scenarios), the calculated index of effectiveness values were 0.978 and 0.680, respectively, corresponding to 2.2% and 32.0% reductions in crashes. In addition to the EB method, naïve study methods (with and without AADT taken into account) were applied to the study data. Results (crash reductions) from these methods also showed that the presence of Gateway Monuments did not have negative impact on traffic safety. However, the use of EB technique should be very careful employed when adopting reference groups from different jurisdictions, as these may affect the validity of EB results. In light of these results, Caltrans may continue to participate in the Gateway Monument Program at its discretion with the knowledge that roadway safety is not impacted by monuments. [ABSTRACT FROM AUTHOR]

In recent years, there has been growing recognition of the effects of weather on the surface transportation system. Although considerable work has been done in quantifying the effects of weather on the highway system, there is still much that remains unknown about the relationship between weather and highway system performance. This paper synthesizes the findings from some of the major efforts in this area. The review of existing studies found consistent patterns that adverse weather reduces traffic speed and increases crash frequencies, while fatal crashes are decreased. A table is then presented which estimates the change in crash frequency and vehicle travel speed resulting from various winter weather conditions, based on a synthesis of earlier work. To estimate the safety and speed adjustment factors of compacted snow, a severity index is also developed. Recognizing the lack of comparability between the results of the studies, the paper concludes with a detailed discussion of avenues for future research which could help to address some of the gaps which currently exist. These challenges include, but are no limited to: quantification of the dynamic layer, development of the relationship between pavement friction and the composition of the dynamic layer, evaluation of the effects of pavement friction on vehicle speed, and evaluation of safety effects of weather conditions above the pavement. [ABSTRACT FROM AUTHOR]

Abstract: A great number of pedestrians are killed or injured in traffic crashes every year in the US. Vehicle crashes involving pedestrians are often more severe than other crashes because pedestrians are unprotected and are hence more likely to suffer injuries or death if struck by a motor vehicle. To improve pedestrian safety, a variety of treatments such as overhead flashing beacons, in-street crossing signs, in-roadway warning lights, and traffic calming measures have been used. One treatment, in-street yield-to-pedestrian channelizing devices (YTPCD), has been used in many states, including Pennsylvania, where approximately 10% of traffic crash fatalities are pedestrians each year. In an effort to improve pedestrian safety, the Pennsylvania Department of Transportation (PennDOT) has widely deployed YTPCD. This study examines the spillover (indirect) effects of such devices on motorist and pedestrian behavior. With data collected from eight sites that did not have but were in the vicinity of YTPCD implementations, analysis results show that such devices have significantly positive spillover effects on pedestrian safety at intersections, but they tend to have negative spillover effects at mid-block locations. Overall, the YTPCD appear to have a positive impact on changing motorist and pedestrian behavior, and merit consideration for future usage of this type of device. [Copyright &y& Elsevier]

Abstract: Problem: To simplify the computation of the variance in before-after studies, it is generally assumed that the observed crash data for each entity (or observation) are Poisson distributed. Given the characteristics of this distribution, the observed value (x i ) for each entity is implicitly made equal to its variance. However, the variance should be estimated using the conditional properties of this observed value (defined as a random variable), that is, f(x i |μ i ), since the mean of the observed value is in fact unknown. Method: Parametric and non-parametric bootstrap methods were investigated to evaluate the conditional assumption using simulated and observed data. Results: The results of this study show that observed data should not be used as a substitute for the variance, even if the entities are assumed to be Poisson distributed. Consequently, the estimated variance for the parameters under study in traditional before-after studies is likely to be underestimated. Conclusions: The proposed methods offer more accurate approaches for estimating the variance in before-after studies. [Copyright &y& Elsevier]

A free-floating bike sharing system is an up-and-coming and marketable solution to promote transport flexibility and health benefits, which many people regard as a realistic way of generating more environmentally-friendly trips. Although many studies have investigated the associations between bike sharing usage and built environment, the existing literature has limited evidence about the relative importance of different built environment elements and their threshold impacts on cycling trips. This study contributes to the literature by proposing a modeling framework to explore the nonlinear impacts of built environment on bike sharing demand. A case study is conducted using Mobike bike sharing data in Chengdu. The analytical results indicate that population density and employment density are the two most significant factors that influence bike sharing usage. Total effects of land use variables rank the highest, followed by accessibility variables and transport facility variables. We then analyze the nonlinear impacts of different built environment elements on bike sharing usage to identify their effective ranges and threshold effects. These findings are important for planning departments to boost the share of non-motorized trips and embrace a cyclist-friendly design. • The intensity and distribution of FFBS usage are measured by the Kernel density estimation technique. • Contributions of different built environment elements to FFBS usage are examined to quantify their relative importance. • Effective ranges and threshold effects of built environment elements are identified through partial dependence plots. • This study offers valuable policy suggestions for planners to effectively promote the share of non-motorized trips. [ABSTRACT FROM AUTHOR]

• Establishing a hybrid model that incorporates the geographical weights into the modeling structure of random forest. • Quantifying the relative importance of built environment elements to intermodal transit trips in each TAZ. • Identifying the effective range and threshold effect of built environment elements at the spatial scale. • Offering valuable implications for planners to formulate targeted policies to increase transit usage. Understanding intermodal transit trip generation is essential to increase the share of long-distance transit trips among urban transportation systems. Although many studies have investigated trip generation, the existing literature still has limited evidence about intermodal transit trips and their nonlinear associations with the built environment over space. This study proposes a decision framework to identify the mean relative importance of socioeconomic attributes and built environment elements as well as their effective ranges and threshold effects at the spatial scale. An empirical study was conducted using large-scale smart card data in Nanjing, China. The modeling results indicate the proposed hybrid model can significantly enhance the predictive power, as compared to traditional models. The mean relative importance of the distance to the nearest metro station ranks the highest among all attributes studied, followed by bus route and land use mix. The effective ranges and thresholds of most built environment elements vary spatially with the upper quartile zones being the largest. [ABSTRACT FROM AUTHOR]

The objective of this study is to investigate impacts of illegal crossings on vehicle operations at unmarked midblock locations. In this study, we first used a speed testing method to examine the significant difference in vehicle speeds in upstream, crossing, and downstream areas. A multinomial logit model was then proposed to identify impacts of vehicle-pedestrian interactions on vehicle behaviors based on speed, distance, and other safety measures. Last, we quantified the probability of different types of vehicle behaviors influenced by illegal crossings in marginal analysis. A case study was conducted in Nanjing, China, by utilizing trajectory data that were extracted from videos collected by an unmanned aerial vehicle. Results indicate that over half of vehicles chose to decelerate for pedestrians when the speed was less than 45 km/h. When the vehicle-pedestrian lateral distance is more than 3 m, vehicles have a higher probability of maintaining the current running status. When vehicles were involved in an interaction with pedestrians in two adjacent lanes, lane changing was significantly restricted and deceleration was the preferred way to yield in approximately 80% of the interactions. In contrast, the probability of lane changing showed an upward trend as the number of pedestrians ahead of a vehicle increased and reached almost 50% with four pedestrians. [ABSTRACT FROM AUTHOR]

The primary objective of this study is to identify the spatio-temporal relationship between metro ridership and built environment by integrating smart card data with online point of interest (POI) data. This study proposes the geographically weighted regression (GWR) model to address the spatial autocorrelation and nonstationarity of metro ridership at the station level. Different from the Euclidean distance (ED) commonly used in the traditional GWR, the Minkowski distance (MD) metric is introduced into the study to measure geographical distance and improve weighting matrix calibrations. This study provides useful insights in adaptive model settings based on selections of spatial range and distance metric parameters. A case study is conducted in Nanjing, China, by utilizing large-scale metro smart card data and POI data obtained from the Gaode map by a web crawler written in Python. The comparative analysis indicates that MD-GWR achieves better goodness-of-fit than the global ordinary least squares (OLS) and traditional GWR in the case of modeling metro ridership. Finally, this study quantifies the impacts of built environment at both spatial and temporal scales, under the proposed modeling structure. We examine the existence of job-housing separation and corresponding areas in spatio-temporal ridership analysis. Impacts of non-commuting activities and intermodal connection on boarding and alighting ridership differ in spatial and temporal ranges. The proposed modeling structure promotes modeling precision and thus enhances the understanding of the relationship between station-level ridership and surrounding built environment, which can provide useful guidance for planning departments and transit agencies to implement targeted policies and create accessible, livable and vibrant communities. • Apply smart card data and POI data to identify metro travel patterns and purposes • Optimize the traditional GWR by using the MD metric in metro ridership modeling • Explore spatio-temporal impacts of built environment on ridership in a broad view • Provide policy implications for metro system and city development [ABSTRACT FROM AUTHOR]

Public transportation is regarded as a mitigation measure for addressing climate change and air quality deterioration. However, it is still necessary to estimate the emissions of transit buses, in particular when heavy loads and long periods of operation result in increased emission levels. Consequently, the primary objective of this study is to establish a method to estimate CO, CO2, HC, NOx emissions of buses with four different fuel types including gas-electric hybrid electric buses (GEHE buses), compressed natural gas buses (CNG buses), EURO 4 heavy-duty diesel engine buses (EURO 4 buses) and EURO 5 heavy-duty diesel engine buses (EURO 5 buses) based on the long short-term memory network. The proposed models can fully consider the time dependence of emissions response to vehicle operation situation. In addition, to evaluate the performance of the proposed models, an effective emission model which also addressed the time dependence of emissions by taking the elapsed time of acceleration and deceleration into account, was developed for emissions on each route for comparison. According to the estimation results, the emission models developed in this study performed better than the compared method in terms of emission rates and average emission factors, whose root mean squared errors (RMSE) were explicitly lower than the compared method, mean absolute percentile errors (MAPE) were lower than 50% of the compared method, and the predicted average emission factors were relatively more accurate than those of the compared models. [ABSTRACT FROM AUTHOR]

Highlights • Large uncertainty on the situations on which the positive effect of obstacle near exit could be observed. • No established relationship between the exit width, obstacle distance and obstacle size/shape. • Verification of the model's prediction with empirical data with humans is largely unexplored. • Researchers have questioned the implementation of obstacles at bottlenecks in real life scenario. • Testing of "obstacles effects" is largely unexplored for outdoor scenarios. Abstract In a pioneering work in Nature journal, a counter-intuitive prediction that escape rates of people under panic conditions will be enhanced if an obstacle such as a column or a barrier is placed on the "upstream" side of an exit was demonstrated through a simulation model. However, the prediction lacked empirical verification. Despite the substantial works in this topic in the past decade, there is currently a lack of knowledge on how and to what extent the obstacle near an exit can enhance the pedestrian outflow at the bottlenecks during emergency escape. Therefore, the aim of this paper is to present a critical review on the performance of an obstacle near an exit and identify future research directions. It is found that although there is a general consensus on the beneficial effect of an obstacle, there is a large uncertainty on the situations on which the positive effect of obstacle could be observed. In addition, verification of the model's prediction with empirical data with humans is still largely unexplored. There is no clear established relationship between the exit width, obstacle distance and obstacle size/shape. Also, quantitative understanding of the nature of the clogging transition due to obstacle is a challenging task. Further, researchers have questioned the implementation of such obstacles at bottlenecks in real life scenario. A systematic approach of optimising architectural adjustments that enhances escape dynamics of pedestrians' crowd in indoor and outdoor public spaces needs to be conducted in future. [ABSTRACT FROM AUTHOR]

Commute behaviors, as the primary part of urban mobility, remains largely underexplored, especially for bike-sharing users. Recent development in data availability open up new possibilities to delve into bike-sharing commuting over long-term periods on a large scale. This study proposes a methodological framework that enables a logical identification of bike-sharing commuting activities and a comprehensive examination of urban built environment effects on shaping commuting patterns. To this end, a series of data mining methods are developed in support of the identification of regular bike-sharing commuting, and the concepts of home-work balance and mobility trend are proposed to describe underlying commuting patterns. The XGBoost model and Necessary Condition Analysis (NCA) method are then adopted respectively to test the sufficiency and necessity of built environment on commuting patterns. The results confirm the massive existence of individual-level bike-sharing commuting activities and the pivotal role of bike-sharing in urban commuting. Also, the spatial distributions of home-work balance and mobility trend driven by job-housing separation show different clustering patterns. Besides, the synergy of sufficiency analysis and necessity analysis investigates the complex interplay of built environment-commuting patterns. This critical analysis of bike-sharing commute provides insights into sustainable transit planning and urban design. • This study proposes a data mining approach to identify bike-sharing commute from trip record data. • This study develops home-work balance and mobility trend to measure commuting patterns. • This study combines necessity analysis and sufficiency analysis to understand built environmental effects. • The findings provide new insights for collaborative planning of bike-sharing and urban built environment. [ABSTRACT FROM AUTHOR]

Addressing the global challenge of traffic crashes necessitates transcending traditional statistical models, which often fail to fully capture the interactions between factors causing crashes. This oversight restricts the predictive accuracy and adaptability of current methodologies. Additionally, there is a notable gap in research that examines the links between behavior-cause relationships and crash injury severity. Our study deploys Natural Language Processing (NLP) and Frequent Pattern (FP) growth algorithm to mine crash narratives for behavior-cause connections, combines with the predictive strength of eXtreme Gradient Boosting (XGBoost) and the interpretative clarity offered by SHapley Additive exPlanations (SHAP), our approach not only predicts crash injury severity with satisfactory precision but also explains the influence of specific behavior-cause and environment conditions on crash outcomes. The integration of NLP and XGBoost, complemented by SHAP insights, has shown promising results with an accuracy of 0.79, outperforming traditional discrete choice models and competes closely with other machine learning approaches, including Support Vector Machines, Random Forest, Categorical Boosting (CatBoost), and Light Gradient Boosting Machine (LightGBM). Through detailed textual analysis and the establishment of a behavior-cause matrix, identifying five broad crash causes linked to 141 specific crash cause with behaviors, we uncover critical patterns such as the prominence of distracted driving in severe crashes. This comprehensive approach not only fills a critical research gap by linking behavior-cause relationships with injury severity but also sets the stage for developing targeted interventions to enhance road safety. • Merges NLP and XGBoost model for crash injury severity prediction. • Identifies crash causes and related driver behaviors using NLP and FP-growth. • Aids in developing traffic safety interventions based on SHAP interpretability. [ABSTRACT FROM AUTHOR]

Bicycle-metro integration, in which bicycling is used as a flexible feeder mode to connect with public transport nodes presents new opportunities for sustainable transportation. It is known that the built environment can influence travel attitudes and choice, yet the empirical evidence for the role of built environment features in shaping the bicycle-metro integration remains rare. Inspired by the idea of text mining, this article is an attempt to demonstrate a data-driven semantic framework to capture key topic-based features of land use and bicycle-metro integrated usage in the vicinity of metro stations as well as their interactions. Latent Dirichlet Allocation topic modeling is analogously implemented here to generate a range of probability-based land use patterns and mobility patterns, and the associations between them are investigated by multivariate linear regression. A case study from Shanghai shows that the mixed land use and diversification of urban functions in the catchment areas of the metro stations can be detected effectively by 11 identified land use patterns. Based on 7 derived mobility patterns, this paper gives a probabilistic explanation to the time-varying properties of the bicycle-metro usage. All of the above thematic topics exhibit notably heterogeneous patterns in spatial distribution. The topic compositions in terms of land use pattern and mobility pattern at the station level reveal the current performance of station areas. Plus, results from the regression analysis confirm that most of the land use patterns that are related to various mixed use have close relationships with mobility patterns of bicycle-metro integration. Yet it is noteworthy that the effects of land use patterns often differ and change over time, namely affecting different mobility patterns. This study gives rise to alternative insights into the synergy between bike sharing and metro, which may help policymakers to develop more targeted TOD strategies. [ABSTRACT FROM AUTHOR]

Road traffic safety is confronted with unique challenges worldwide due to the varied road network, high population density, and the rapid increase in vehicle numbers. This necessitates an effective evaluation of traffic safety at the road segment level to improve traffic safety management. Existing safety indicators, such as crash frequency (which lacks real-time availability) and Surrogate Safety Measures (focused on micro-level interactions), fall short in quantifying the overall safety status of road segments. To overcome these limitations, we introduce the concept of entropy, typically used to measure disorder in complex systems, into road traffic safety assessment. Our research presents a novel macroscopic safety indicator named 'velocity entropy,' along with its calculation method, which involves discretization and the Parzen window technique. The efficacy of velocity entropy is validated using the UTE dataset, a high-fidelity trajectory dataset from China, showing a more significant correlation with traffic conflicts than other macroscopic traffic properties. Notably, velocity entropy can track the temporal and spatial evolution of traffic safety, providing insights into the dynamic development of traffic risks. Overall, velocity entropy offers an innovative, practical, and theoretically meaningful tool for assessing and predicting road safety. [ABSTRACT FROM AUTHOR]

As the COVID-19 pandemic worsened, many people saw bikes as one of the safest means of transportation in the hard-hit cities. All the bike sharing utilization patterns during the pandemic are worthy of careful attention. However, there is still a lack of comprehensive understanding of niche but notable cycling behaviors, such as multi-person round trip (MPRT), defined as two or more cyclists intentionally riding together then returning bikes to the original docking station. This study extends the relevant literature by firstly proposing a MPRT identification framework based on individual bike sharing trip records, with consideration of interpersonal relationships between co-travelers, as well as the specificity of round trips against one-way trips. Taking New York City as a case study, this study examines the changes over space and time of MPRT frequencies from 2019 (i.e. pre-pandemic period) to 2020 (i.e. pandemic period), and the reasons for it. Notably, special consideration of the aforementioned analysis is paid to the influence of the real-time situation of COVID-19 in terms of cases, deaths, hospitalizations, and tests. Results reveal that (1) the MPRT frequencies obey a long tail distribution, both prior to and during the COVID-19 outbreak; (2) the group size, temporal patterns and co-traveler community are profoundly affected by the COVID-19 outbreak; (3) four indicators related to COVID-19 show different influences on co-travelers over time; (4) bike sharing availability and personal economic situation are closely related with MPRT frequencies. These findings can help develop more targeted strategies for improving the operation of a bike sharing system to meet the possible diversified demands of cyclists during the future pandemics. [ABSTRACT FROM AUTHOR]

This study consisted of a stated-preference questionnaire survey to investigate road rage among Chinese drivers. A total of 5157 Chinese drivers completed the survey. The Driving Anger Scale (DAS) and Driving Anger Expression Inventory (DAX) were used to measure road rage under various anger-provoking situations. Meanwhile, the theory of planned behavior (TPB) was adopted to explore respondents' attitudinal perception towards road rage behaviors. Three TPB variables, i.e., behavioral attitude (BA), subjective norm (SN), and perceived behavioral control (PBC), were used to describe drivers' perceived anger levels, driving habits, self-efficacy, and perceived controllability of road rage, respectively. Additionally, aggressive reaction (AR) was introduced to describe drivers' inclination to express road rage. Then, descriptive analyses were performed to examine the connection between individual characteristics on road rage. Results exhibited these variables have different between-group correlation and significant relationships with gender, age group, education, driving years, professional or not, daily driving hours, and vehicle type. Also, outcomes from structural equation models (SEMs) demonstrated that the above four descriptive variables in the modified TPB model could be linked to drivers' intention to exhibit road rage behaviors. Finally, countermeasures for controlling and curing road rage were proposed based on the findings. [ABSTRACT FROM AUTHOR]

The advent of autonomous vehicles (AVs) has complicated traffic flow and raised safety concerns, there is a pressing need to enhance traffic efficiency alongside ensuring robust safety. This paper proposes a dynamic longitudinal control strategy for AVs, based on real-time accident risk assessments. Data from traffic flow, road characteristics, and weather conditions are analyzed using a random forest algorithm, producing a real-time accident risk identification model through a support vector machine. In addition, the Perceived Risk Field Model (PRFM), a car-following model calibrated using the NGSIM dataset, considers vehicle distance, velocity, relative velocity, and acceleration. These models underpin our proposed longitudinal control strategies, tailored to variable accident risk levels. Under the normal risk control strategy, the PRFM outperforms the Intelligent Driver Model (IDM) and the Cooperative Adaptive Cruise Control Model (CACC) in stability and driving comfort. For high accident risk scenarios, our approach shows improved collision avoidance and traffic oscillation mitigation. Simulations indicate that the high accident risk control strategy offers better safety and stability than the normal risk strategy. Furthermore, traffic flow diagrams suggest an increase in road capacity as AV penetration rates rise, pointing to the promising efficiency of our proposed solution. This research provides a robust framework for AV operation, ensuring optimal traffic safety and efficiency. • A real-time accident risk identification model tailored for AVs using machine learning with high predictive accuracy. • Development of a car-following model based on safety potential field and validation using real-world datasets. • Adaptive longitudinal control strategies for AVs, capable of adjusting car-following spacing based on real-time risk. • Framework with risk identification, behavior modeling, and control strategy, providing an integrated approach to AV safety. [ABSTRACT FROM AUTHOR]

• Enhanced dynamic Forward Collision Warning (FCW) model tailored for hazmat truck drivers with varying driving characteristics and risk levels. • Multi-objective optimization method employed to determine the optimal warning time considering real-time driving risk, driver acceptance, and driving comfort. • Differentiation in optimal warning times for driver categories under mid-level and low-level risks, while similar warning time for high-level risks. • Development of user profiles based on historical driving records improves driver trust and enhances driving safety with ADAS systems. The risky driving behavior of hazmat truck drivers is a crucial factor in many severe traffic accidents. In-vehicle Advanced Driving Assistance Systems (ADAS), integrating vehicle active safety and driver assistance technology, has been installed into hazmat trucks aiming to reduce driving risks during emergencies. This paper presents an enhanced dynamic Forward Collision Warning (FCW) model tailored for hazmat truck drivers with different driving characteristics and risk levels. Our objective is to determine the optimal moment to alert drivers during risky situations. The novelty of our approach lies in analyzing the driver's response mechanism to the warning by considering their characteristics and real-time driving risk levels. We employ a multi-objective optimization method that integrates real-time driving risk, driver acceptance, and driving comfort to calculate the optimal warning time. Our findings indicate that the appropriate warning time is similar for all drivers under high-level risks, while significant differentiation exists for different driver categories under mid-level and low-level risks. Additionally, aggressive drivers tend to follow leading vehicles closely and exhibit lower deceleration intentions when faced with dangers compared to normal and cautious drivers. Our research outcomes enable the development of user profiles for hazmat truck drivers based on extensive historical driving records, facilitating the analysis of driver response differences to FCWs. This enhances driving safety and improves driver trust in ADAS systems. [ABSTRACT FROM AUTHOR]

Agent-based approach is a popular tool for modelling and developing large-scale distributed systems such as urban traffic control system with dynamic traffic flows. This study proposes a multi-agent-based approach to optimize urban traffic network signal control, which utilizes a mathematical programming method to optimize the signal timing plans at intersections. To improve the overall network efficiency, we develop an online agent-based signal coordination scheme, underpinned by the communication among different intersection control agents. In addition, the initial coordination scheme that pre-adjusts the offsets between the intersections is developed based on the historical demand information. Comparison and sensitivity analysis are conducted to evaluate the performance of the proposed method on a customized traffic simulation platform using MATLAB and VISSIM. Simulation results indicate that the proposed method can effectively avoid network oversaturation and thus reduces average travel delay and improves average vehicle speed, as compared to rule-based multi-agent signal control methods. [ABSTRACT FROM AUTHOR]

Introduction Visual attention to the driving environment is of great importance for road safety. Eye glance behavior has been used as an indicator of distracted driving. This study examined and quantified drivers' glance patterns and features during distracted driving. Method Data from an existing naturalistic driving study were used. Entropy rate was calculated and used to assess the randomness associated with drivers' scanning patterns. A glance-transition proportion matrix was defined to quantity visual search patterns transitioning among four main eye glance locations while driving (i.e., forward on-road, phone, mirrors and others). All measurements were calculated within a 5 s time window under both cell phone and non-cell phone use conditions. Results Results of the glance data analyses showed different patterns between distracted and non-distracted driving, featured by a higher entropy rate value and highly biased attention transferring between forward and phone locations during distracted driving. Drivers in general had higher number of glance transitions, and their on-road glance duration was significantly shorter during distracted driving when compared to non-distracted driving. Conclusions Results suggest that drivers have a higher scanning randomness/disorder level and shift their main attention from surrounding areas towards phone area when engaging in visual-manual tasks. Practical applications Drivers' visual search patterns during visual-manual distraction with a high scanning randomness and a high proportion of eye glance transitions towards the location of the phone provide insight into driver distraction detection. This will help to inform the design of in-vehicle human-machine interface/systems. [ABSTRACT FROM AUTHOR]

Most bicycle accidents are inextricably bound up with risky riding behaviors, which crossing the street illegally at unprotected mid-block locations is nothing to sneeze at. Compared with cyclists crossing the street at the crosswalk or intersections, there is a huge risk of accidents when they ignore or disobey road rules and across recklessly. Yet, the misbehavior of cyclists is an under-explored area in cyclist research due to the limited availability of detailed cycling data. This study creatively develops a GPS-based detection framework to capture risky street-crossing actions for the cyclists from large-scale bike sharing trajectory data. A data-driven modeling approach, based on structural topic modeling (STM), is developed to reveal the complexity and regularity of cyclists' habitual risky crossing behavior. Since objective built environment is one of the key factors associated with cycling, another goal of this paper is to apply a gradient boosting decision tree (GBDT) model to disentangle how the features of built environment may influence the frequency of risky crossing events. The case study results show that risky street-crossing behavior is prevalent in bicycle traffic – for example, 16.94% of cycling trips are involved in illegal crossing action. Most cyclists engage in illegal crossing behavior at the approximate central part of the streets and during the day, which reveals the presence of heterogeneity over space and time. Strong correlations between commuting activities and risky street-crossing behaviors are identified from topic modeling. Meanwhile, the latent illegal crossing patterns unraveled here highlight that typical reasons for committing the risky riding action include the lure of the travel destination across the road and the inconvenience of riding round in distant legal crossing facilities. GBDT findings provide new insights on the existence of the association between built environment and cyclists' illegal crossing action. The places related employment and catering play a dominant role in contributing risky street-crossing behavior, and the influences of road length, road level, bus stop and metro station are not neglectable. Most built environment attributes show nonlinear correlations with crossing frequency. It is anticipated that this study would successfully shed a first light on the pattern of cyclists' risky street-crossing behavior at the metropolitan scale, and compliment engineering practices to improve crossing behaviors and bicycle safety. [ABSTRACT FROM AUTHOR]

Bike-sharing systems have become an indispensable transportation mode due to the environmental friendliness and shareability in the sustainable cities development process. However, the asymmetry of people's travel patterns during the morning and evening rush hours has contributed to the imbalance in bike inventory. Consequently, station rentals and returns require redistribution to address the rebalancing of bikes. In this paper, a user-based repositioning method through a bi-level programming model is proposed. With the objective of minimizing the repositioning workload, the upper-level model yields redistribution proportions that enable the balance between rentals and returns at the station. In addition, aiming at maximum user profit, the lower-level model calculates a redistribution matrix between station pairs and provides recommended stations for users. Finally, three levels of evaluation indicators for the bike-sharing system, operators, and users are presented. The results indicate that the proposed user-based repositioning is remarkably effective in improving the bike inventory balance and the station's turnover rate. This study provides a novel idea for the bike-sharing repositioning problem and contributes to the improvement of urban transportation. • A user-based repositioning method using a bi-level programming model is proposed. • The proposed method is evaluated in terms of users, operator and bike-sharing system. • The effectiveness of the model is validated in the Philadelphia bike-sharing system. • The suggested polices could accommodate both repositioning costs and efficiency. [ABSTRACT FROM AUTHOR]

This study investigates the effects of exit layout designs on children pedestrians' exit choice under normal and emergency scenarios. A series of controlled experiments was performed with 40 children aged 4–5 years old. The experimental setup included a square room with two exits. Exit designs considering six types of exit layouts were examined. The evacuation performance and the herding effects were quantified by analyzing the extracted trajectories from the video of the experiments. Results revealed that there is a significant influence of exit layouts on both evacuation performance and herding effects. In addition, comparing the results from the first three exit layout designs with the findings from past adults' exit choice experiments, it was discovered that the herding effects were, in general, more significant in children pedestrians but varied by different exit layouts. The findings of this study could be a valuable resource to architects or planners to plan and design exits for schools or kindergartens. • An empirical investigation on the exit choice behaviors of children pedestrians. • The mixed use of middle and corner exits tended to reduce the evacuation efficiency of children pedestrians. • The usage patterns of exits of children pedestrians were investigated and compared with adults pedestrians. [ABSTRACT FROM AUTHOR]

Architectural features such as merging corridors form an important component in floor plans of any major public infrastructure. Previous studies on documented crowd disasters have highlighted that passage restriction such as merging corridors can have negative impacts on the efficiency of evacuation process. However, limited data exists on merging process in the literature. This study aims to address this issue through empirical data collection and analysis of merging process in a controlled laboratory walking experiments. A series of experiments were conducted with different merging angles (60°, 90° and 180°) and with different desired speed (normal and slow running). The experiments indicated that pedestrians tend to reduce speeds within merging areas. With higher merging angle, there is greater reduction in speed in the merging area. Speed reduction is statistically significant with merging angles and desired speed. The speed reduction had an effect on the flow rate with reduced flow rate observed for higher merging angle. The empirical results from this study can be used to develop and test pedestrian crowd simulation models. [ABSTRACT FROM AUTHOR]

Crowd egress at narrow exit is a popular research topic, due to its intrinsic importance in architectural designs and building codes. However, relatively few studies have been conducted to verify the performance of pedestrian models for crowd escape at exits, especially relating to different exit designs. This paper aims to verify the applicability of a microscopic pedestrian simulation model, Social Force Model (SFM), embedded in Viswalk software to reproduce the effect of exit design on egress flow under normal and emergency conditions. Empirical data from controlled experiments considering the effects of obstacles size and location of exits under normal and emergency conditions were tested and compared with the simulation from the SFM. Results indicated that after parameter optimization, Viswalk simulation model can provide reasonable estimates for crowd escape under normal situations with a mean RMSE value 1.97s for total evacuation time. However, the simulation model was less capable in reproducing the emergency condition. As compared to the empirical data, clogging events were less spotted under emergency in the simulation. Faster-is-slower effects were not found in both empirical and simulation scenarios. In addition, the exit location effects from simulation data agreed with empirical data, corner exits were more efficient than middle exits under both situations. Meanwhile, the obstacle effects, as observed in empirical data, were less reproduced in the simulation, especially under emergency conditions. The results suggest that the application of the Viswalk model in simulating emergency situations needs scrutiny and further investigations in the future with empirical data. • The effects of different architecture adjustments of exits including the exit position, obstacle size and position on pedestrian flow dynamics. • A comprehensive experimental dataset to validate pedestrian model's performance. • The verification for Viswalk's pedestrian simulation model and the parameter optimization. [ABSTRACT FROM AUTHOR]