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This paper studies a class of gradient-descent heuristic algorithms for the bi-level demand adjustment problem (DAP), which seeks to adjust origin-destination (OD) matrices based on observed link flows in congested transportation networks. We first present a general gradient-descent solution framework for the bi-level DAP and then examine and further develop its two building blocks, namely, the gradient approximation and stepsize calculation. This paper presents two gradient approximation and four stepsize calculation methods, of which two stepsize methods are newly developed. Similarities and differences between these algorithms, as well as the relevant implementation issues are discussed in great detail. The numerical results show that algorithms employing the new stepsize calculation strategies consistently outperform existing algorithms in terms of both computational precision and efficiency., Author(s): Lan Cheng [1]; Jun Xie [1]; Jun Huang (corresponding author) [1]; Liyang Feng [1]; Qianni Wang [2]; Hongtai Yang [1] 1. Introduction OD matrices (ODM) are essential inputs for [...]

Shared bikes can help cities achieve carbon neutrality goals. Cleaning and disinfection are vital procedures of the maintenance of shared bikes, especially during the COVID-19 pandemic because shared bikes could be a transmission intermediary of viruses. This study proposes an optimization model of the cleaning and disinfection scheme of the dockless shared bikes. The disinfection is assumed to be performed at night, when the usage is lowest. By regarding the disinfection staff as traveling salesmen, the model is formulated as an extension of the Multidepot Multiple Traveling Salesman Problem (MDMTSP). The objective function is to minimize the total cost; which consists of the cost associated with the working time and per-capita cost of the disinfection staff. A heuristic algorithm combining k-means clustering and genetic algorithm (K-GA) is adopted to find the lower bound solution. Then, the K-GA-adjustment algorithm has been adopted to find the solutions that satisfy the constraints. To reduce the computing time needed, an approximate function for the lower bound of the optimal number of disinfection staff is obtained by constructing a Continuous Approximation (CA) model. A case study based on real location data of shared bikes in Chengdu, China, is performed to show how the maintenance department could adopt the optimization framework to design an efficient scheme to clean and disinfect the shared bikes., Author(s): Rong Zheng [1]; Hongtai Yang (corresponding author) [1]; Lanzhen Jiang [1]; Jinghai Huo [1]; Xiaoqian Lu [1]; Malik Muneeb Abid [2] 1. Introduction In recent years, with the increase [...]

This study aims to find the potential of Bus Rapid Transit (BRT) to attract the vehicle owners from their personal vehicles i.e., motorcars and motorcycles. Stated preference survey (questionnaires) and interviews were conducted at BRT (Metro Bus) Stations for the prediction of the individuals shifting from their private vehicles to BRT. Questions were designed critically as per the requirements of the research related to numerous aspects of BRT use i.e., vehicle ownership of the travelers, driving license holder, demographic characteristics, choice to use BRT if the fare increases, trip purpose and their prior mode of transportation for the same trip. A total of 374 responses, as per the population of the study area (Islamabad-Rawalpindi, Pakistan), were collected. The Multinomial Logistic Regression (MNL) model has been employed for four categories of vehicle ownerships i.e., “Car owners using BRT”, “Bike owners using BRT”, “Both Car and Bike owners using BRT” and the last one which has been taken as reference category is “BRT users with no vehicle ownership”. The analysis indicated that BRT has attracted considerably private vehicle users specially the bike owners. Some socio-economic factors like income and residence location (accessibility) additionally have a major effect on the selection of BRT. In addition, it has been observed that fare increase can alter the mode choice of the BRT users and they will again prefer their own vehicles. The Travel choice model developed in the study can be very useful for policy makers and transport planners to enhance the BRT service and attraction, to mitigate traffic congestion and car ownership.

Bike-sharing holds promise for available and healthy mobility services during COVID-19 where bike sharing users can make trips with lower health concerns due to social distancing compared to the restricted transportation modes such as public transit and ridesharing services. Leveraging the trip data of the Divvy bike-sharing system in Chicago, this study exploresspatially heterogeneous effects of built environment on bike-sharing usage under the pandemic. Results show that the average weekly ridership declined by 52.04%. To account for the spatially heterogeneous relationship between the built environment and the ridership, the geographically weighted regression (GWR) model and the semiparametric GWR (S-GWR) model are constructed. We find that the S-GWR model outperforms the GWR and the multiple linear regression models. The results of the S-GWR model indicate that education employment density, distance to subway, COVID-19 cases, and ridership before COVID-19 are global variables. The effects between ridership and the built environment factros (i.e., household density, office employment density, and the ridership) vary across space. The results of this study could provide a useful reference to transportation planners and bike-sharing operators to determine the high bike-sharing demand area under the pandemic,thus adjusting station locations, capacity, and rebalancing schemes accordingly., Author(s): Hongtai Yang [1]; Zishuo Guo (corresponding author) [1]; Guocong Zhai [2]; Linchuan Yang [3]; Jinghai Huo [1] 1. Introduction The outbreak of COVID-19 has seriously threatened the lives of [...]

Station-level ridership modeling is one of the ways to forecast metro ridership and reveal how factors influence ridership. Previous studies assumed that the relationships between the dependent variable and independent variables are either global or local, as indicated by the global model or the geographically weighted regression (GWR) model. This study explores the possibility that some independent variables have spatially varying relationships with metro ridership while others have constant relationships by employing the mixed GWR model. Data from the Chicago metro system were used. To establish an effective forecasting model, possible influencing factors are collected. OLS model results indicate that the proportion of recreational jobs to total jobs, number of bus stops, employment density, number of high- income workers, and the type of station (transfer or terminal) are significant variables influencing station-level metro ridership. By using the mixed GWR model, we find that the proportion of recreational jobs to total jobs is a global variable while the others are local variables. By comparing the results of mixed GWR, full GWR, and OLS models, we find that mixed GWR fits the data better and the residuals are less correlated. However, results of cross-validation indicate that the prediction power of the OLS model is better than that of the full and mixed GWR models. Key words: metro ridership, transit demand, land use, built environment, spatial variation. La modelisation de l'achalandage au niveau des stations de metro est l'une des fa^ons de prevoir l'achalandage dans le metro et de representer comment les facteurs influent sur l'achalandage. Les etudes anterieures supposent que les relations entre la variable dependante et les variables independantes est globale ou locale, comme l'indique le modele global ou le modele de regression geographiquement ponderee (RGP). Cette etude explore la possibilite que certaines variables independantes aient une relation spatialement variable avec l'achalandage du metro, tandis que d'autres ont une relation constante en utilisant le modele de RGP mixte. Les donnees du reseau de metro de Chicago sont utilisees. Pour etablir un modele previsionnel efficace, des facteurs determinants possibles sont obtenus. Les resultats du modele des moindres carres ordinaires (MCO) indiquent que la proportion d'emplois recreatifs par rapport aux emplois totaux, le nombre d'arrets d'autobus, la densite d'emploi, le nombre de travailleurs a revenu eleve et le type de station (de transfert ou station terminale) sont des variables importantes qui influent sur l'achalandage des stations de metro. En utilisant un modele de RGP mixte, nous constatons que la proportion d'emplois recreatifs par rapport a l'ensemble des emplois est une variable globale, tandis que d'autres sont des variables locales. En comparant les resultats des modeles de RGP mixte, de RGP complet et des MCO, nous constatons que le modele RGP mixte reproduit mieux les donnees et que les residus sont moins correles. Toutefois, les resultats de la validation croisee indiquent que la puissance predictive du modele MCO est meilleure que celle des modeles RGP complet et RGP mixte. [Traduit par la Redaction] Mots-cles: achalandage du metro, modelisation au niveau de la station, utilisation du terrain, modele de regression geographiquement ponderee mixte, environnement bati., Introduction The metro is one of the major travel modes in big cities because of its fast speed, timeliness, safety, and environment-friendly characteristics. Ridership is one of the key inputs [...]

Finding the shortest path of the traveling salesman problem (TSP) is a typical NP-hard problem and one of the basic optimization problems. TSP in three-dimensional space (3D-TSP) is an extension of TSP. It plays an important role in the fields of 3D path planning and UAV inspection, such as forest fire patrol path planning. Many existing studies have focused on the expected length of the shortest path of TSP in 2D space. The expected length of the shortest path in 3D space has not yet been studied. To fill this gap, this research focuses on developing models to estimate the expected length of the shortest path of 3D-TSP. First, different experimental scenarios are designed by combining different service areas and the number of demand points. Under each scenario, the specified number of demand points is randomly generated, and an improved genetic algorithm and Gurobi are used to find the shortest path. A total of 500 experiments are performed for each scenario, and the average length of the shortest path is calculated. The models to estimate the expected length of the shortest path are proposed. Model parameters are estimated and k-fold cross-validation is used to evaluate the goodness of fit. Results show that all the models fit the data well and the best model is selected. The developed models can be used to estimate the expected length of the shortest path of 3D-TSP and provide important references for many applications., Author(s): Hongtai Yang [1]; Xiaoqian Lu [1]; Xinan Zhou [1]; Rong Zheng [1]; Yugang Liu (corresponding author) [1]; Siyu Tao [1] 1. Introduction The traveling salesman problem (TSP) is a [...]

As a sustainable mode of transportation, subways bring great convenience to the society. Although there have been many studies examining the relationship between the built environment and the station-level ridership, those studies focused mainly on the ridership, which is defined as the number of trips for each station. While ridership is an important indicator for evaluating subway demand, passenger-distance is another critical indicator that incorporates distance into demand evaluation, which has not yet been fully explored. To fill this gap, this paper investigates the relationship between the built environment around stations and the station-level passenger-distance (SLPD). As noted in previous studies, the relationship between the built environment and travel demand can vary by space. Therefore, a geographically weighted regression (GWR) model and a mixed geographically weighted regression (MGWR) model have been used to explore this spatially varying relationship using Chengdu, China, as an example case. The results were compared with that of an ordinary least squares (OLS) model. The comparison shows that the MGWR model that considers both global and local variables has the best goodness of fit. Results also show that 11 of the 25 potential variables are significantly related to SLPD. The accessibility of the station, station type, such as transfer or terminal, number of bus stops, number of restaurants, density of building area, density of the national road network, and density of the provincial road network, all have a positive correlation with SLPD. Meanwhile, the variables, whether it is a newly opened subway station, density of living points of interest (POIs), and density of railroad network, are all negatively correlated with SLPD. Ten of the eleven significant variables (except accessibility) have spatially varying relationships with SLPD. These findings can serve a useful reference for transportation planners for the demand evaluation., Author(s): Hongtai Yang [1]; Zhihao Zhao (corresponding author) [1]; Chaozhe Jiang (corresponding author) [1]; Yi Wen [2]; Malik Muneeb Abid [3] 1. Introduction With urbanization, a growing number of cars [...]

Direct ridership models can predict station-level urban rail transit ridership. Previous research indicates that the direct modeling of urban rail transit ridership uses different coverage overlapping area processing methods (such as naive method or Thiessen polygons), area analysis units (such as census block group and census tract), and various regression models (such as linear regression and negative binomial regression). However, the selection of these methods and models seems arbitrary. The objective of this research is to suggest methods of station-level urban rail transit ridership model selection and evaluate the impact of this selection on ridership model results and prediction accuracy. Urban rail transit ridership data in 2010 were collected from five cities: New York, San Francisco, Chicago, Philadelphia, and Boston. Using the built environment characteristics as the independent variables and station-level ridership as the dependent variable, an analysis was conducted to examine the differences in the model performance in ridership prediction. Our results show that a large overlap of circular coverage areas will greatly affect the accuracy of models. The equal division method increases model accuracy significantly. Most models show that the generalized additive models have lower mean absolute percentage errors (MAPE) and higher adjusted R[sup.2] values. By comparison, the Akaike information criterion (AIC) values of the negative binomial models are lower. The influence of different basic spatial analysis unit on the model results is marginal. Therefore, the selection of basic area unit can use existing data. In terms of model selection, advanced models seem to perform better than the linear regression models., Author(s): Hongtai Yang [1]; Chaojing Li [1]; Xuan Li [1]; Jinghai Huo [1]; Yi Wen [2]; Emma G. P. Sexton [2]; Yugang Liu (corresponding author) [1] 1. Introduction Urban rail [...]

Direct demand modeling is a useful tool to estimate the demand of urban rail transit stations and to determine factors that significantly influence such demand. The construction of a direct demand model involves determination of the catchment area. Although there have been many methods to determine the catchment area, the choice of those methods is very arbitrary. Different methods will lead to different results and their effects on the results are still not clear. This paper intends to investigate this issue by focusing on three aspects related to the catchment area: size of the catchment area, processing methods of the overlapping areas, and whether to apply the distance decay function on the catchment area. Five catchment areas are defined by drawing buffers around each station with radius distance ranging from 300 to 1500 meters with the interval of 300 meters. Three methods to process the overlapping areas are tested, which are the naïve method, Thiessen polygon, and equal division. The effect of distance decay is considered by applying lower weight to the outer catchment area. Data from five cities in the United States are analyzed. Built environment characteristics within the catchment area are extracted as explanatory variables. Annual average weekday ridership of each station is used as the response variable. To further analyze the effect of regression models on the results, three commonly used models, including the linear regression, log-linear regression, and negative binomial regression models, are applied to examine which type of catchment area yields the highest goodness-of-fit. We find that the ideal buffer sizes vary among cities, and different buffer sizes do not have a great impact on the model's goodness-of-fit and prediction accuracy. When the catchment areas are heavily overlapping, dividing the overlapping area by the number of times of overlapping can improve model results. The application of distance decay function could barely improve the model results. The goodness-of-fit of the three models is comparable, though the log-linear regression model has the highest prediction accuracy. This study could provide useful references for researchers and planners on how to select catchment areas when constructing direct demand models for urban rail transit stations., Author(s): Hongtai Yang [1]; Xuan Li [1]; Chaojing Li [1]; Jinghai Huo [1]; Yugang Liu (corresponding author) [1] 1. Introduction Transit-oriented development (TOD) plays a pivotal role in urban planning. [...]

In this paper, a bus priority signal control (BPSC) method based on delays of passengers and pedestrians at adjacent intersections, is proposed. The influences of BPSC on passenger and pedestrian delay at adjacent intersections under the condition of coordinated control of green waves are studied. The implementation of BPSC at intersections not only reduces the delay of bus passengers, social vehicle passengers and pedestrians, but also improves the traffic flow of priority buses and social vehicles at downstream intersections. This study takes the green phase extension as an example of the active BPSC strategy, and analyzes three cases of priority vehicles reaching downstream intersection. Firstly, passenger and pedestrian delays at adjacent intersections are calculated under different traffic situations. Secondly, models with the goal of maximizing the reduced total delays are established. Thirdly, three algorithms are used to solve the problem to obtain the optimal signal timing adjustment scheme at upstream intersections. Ultimately, the result shows that the BPSC can effectively reduce pedestrian delays at intersections, protect the rights and interests of pedestrians, reduce the delays of priority vehicles, and maximize the reduced total delay., 1. Introduction In order to alleviate urban traffic congestion and traffic pollution, it is particularly important and urgent to develop urban transit and to give transit priority accordingly. BPSC is [...]

As the most prevalent physical activity and transportation mode for older people, walking is considered to have multiple health and well‐being benefits. Previous studies used separate models to assess the built‐environment determinants of a battery of walking behavior measures, such as walking frequency and duration. In a departure from them, this study develops a system of equations, which is estimated by seemingly unrelated regression, to determine the built‐environment factors that significantly influence two correlated walking behavior measures (including walking frequency and duration) of older adults in Xiamen (a medium‐sized Chinese city) based on data from the Travel Survey of Xiamen Residents 2015 and built‐environment geo‐data. The results show the following: (1) the walking frequency and duration of older adults are affected by the built environment and socio‐demographic characteristics; (2) land‐use mix, intersection density, and bus route density positively influence older adults' walking frequency and duration; (3) distance to the commercial center adversely impacts the walking frequency and duration; and (4) the built environment has similar effects on the two measures. This study offers a worthwhile reference for policy intervention to promote older adults' walking activities, thereby contributing to active and healthy aging. [ABSTRACT FROM AUTHOR]

This study aims to investigate the effect of coronavirus disease 2019 (COVID-19) on the Chinese public's mental health during its early stage. We collected the data through an online questionnaire survey. Specifically, we adopted the impact of event scale-revised (IES-R) and state-trait anxiety inventory (STAI) to assess symptomatic responses to exposure to traumatic life events and public anxiety, respectively, in the COVID-19 pandemic outbreak. Then, we evaluated the differences in the scores among various socio-demographic groups using Kruskal-Wakkis H tests and t -tests and analyzed the IES-R, state anxiety (SA) score, and trait anxiety (TA) score using the Pearson correlation analysis. Finally, we conducted a path analysis to determine the mediating role of post-traumatic stress disorder symptoms (measured by the IES-R) in the relationship between TA and SA. The results show that the average of the SA and TA scores were 48.0 ± 10.4 and 38.0 ± 8.2, respectively; the respondents who suffered from mild, moderate, and severe psychological impacts because of the health crisis accounted for 21.9, 5.2, and 13.1%, respectively; farmers have the highest IES-R score than others; people with the highest income have the lowest SA level; a significant positive correlation existed between the IES-R and STAI scores; and TA produces both direct and indirect (through the IES-R) effects on SA. Overall, the general Chinese public exhibited much higher anxiety levels than normal in the early days of the pandemic outbreak. Accordingly, we strongly recommend psychological counseling and intervention support to mitigate the adverse psychological impacts of such an event. [ABSTRACT FROM AUTHOR]

Differing from the conventional fixed-route transit, Demand Responsive Connector (DRC) is scheduled to pick up passengers based on requests, and delivers them to a common destination (e.g., a metro station). The demand for DRC is affected by not only the fare and travel time, but also time reliability. The latter’ impact is non-negligible because of the uncertainty nature of DRC on collecting the temporally and spatially distributed passengers. This paper explicitly introduces an elastic demand function embedded with a time deviation penalty model. Based on that, one-vehicle and two-vehicle DRC models are formulated to maximize the social welfare. The decision variables include fare, service area, and operating cycle time. Augmented Lagrange Multiplier Method is applied to solve the optimization problem. Simulation is performed to verify the validity of the models. In numerical studies, sensitivity analysis unveils the influences of several key parameters, e.g., potential demand density, vehicle capacity, line-haul distance and elasticity factors. By comparison, threshold values of demand density are revealed for the applicability of the one-vehicle and two-vehicle schemes. The proposed model provides a decision-making tool for DRC operators considering local conditions. [ABSTRACT FROM AUTHOR]

Although many studies have investigated the correlations between injury severities and seat positions, few researchers explored the correlates of injury severities (e.g., seat positions) within a crash that results in multiple occupant injuries. Therefore, we examine the injury correlates within and between crashes, and study the correlations between seat positions and occupant injury severity by constructing a hierarchical ordered probit model. A total of 20,327 occupant injuries in 16,405 motor vehicle crashes in South Australia (2012 − 2016) are used. The results of this study indicate that the rear left passenger seat is associated with a 7.66% higher chance of getting injured (including moderate and severe injury), and the front left passenger seat is associated with a 2.94% higher chance of getting injured compared with the driver seat. Besides, the higher injury chances for other passenger seats including the rear right and rear middle seats are 4.97% and 4.74%, respectively, compared with the driver seat. Thus, this study offers passengers insightful suggestions about how to protect themselves by choosing the right passenger seat in a vehicle. [ABSTRACT FROM AUTHOR]

High volume of traffic is loaded on urban highway during peak hours as well as the ramp connecting main road and auxiliary road of highway. Once the demand exceeds the capacity of the ramp, congestion may happen on both main road and the auxiliary road. To deal with this problem, an new urban highway ramp, switchable ramp (SR) that can switch between on and off condition, is proposed in this paper. A macroscopic time-space discrete model is built in this paper to model this process by dividing the studied time period into short time periods and the studied road segment into small sections. The impact of the type of ramp on upstream and downstream traffic flow is analyzed. A segment of urban highway in Chengdu, China, is used as a case study. The traffic flow data in peak hours is collected and used. Mathematical analysis proves that the SR system proposed in this paper can reduce the total travel time (TTT) in the studied area. Results show that the SR system is more useful when the traffic volume is high. [ABSTRACT FROM AUTHOR]

Unreported minor crashes have importance as a surrogate for more serious crashes that require infrastructure, education, and enforcement strategies; and they still inflict damages. To study factors that influence underreporting, cause, and severity of minor crashes; a survey was performed in Kunming and Beijing to collect self-reported personal characteristics and crash history data of the three major urban road users in China: automobile drivers, bicycle riders and electric bike (e-bike) riders. Underreporting rates of automobile to automobile, automobile to non-motorized vehicle, and non-motorized vehicle to non-motorized vehicle crashes are 56%, 77% and 94%, respectively. Minor crashes with higher reported injury severity levels are more likely to be reported. E-bike riders without a driver's license are more likely to cause crashes. Licensing and education could be an effective way to reduce their crashes. The party that is not at fault in a crash is more likely to sustain high level of injury. [ABSTRACT FROM AUTHOR]

As the development of traffic detection technology, recent research is directed to a new generation of signal control systems supported by new traffic data. One of these directions is dynamic predictive control by incorporating short-term prediction capability. This paper focuses on investigating dynamic platoon dispersion models which could capture the variability of traffic flow in a cross-sectional traffic detection environment. The dynamic models are applied to predict the evolution of traffic flow, and further used to produce signal timing plans that account not only for the current state of the system but also for the expected short-term changes in traffic flows. We investigate factors affecting model accuracy, including time-zone length, position of upstream traffic detection equipment, road section length, traffic volume, turning percentages, and computation time. The impact of these factors on the model’s performance is illustrated through a simulation analysis, and the computation performance of models is discussed. The results show that both the dynamic speed-truncated normal distribution model and dynamic Robertson model with dynamics outperform their respective static versions, and that they can be further applied for dynamic control. [ABSTRACT FROM AUTHOR]

Along with the rapid development of Intelligent Transportation Systems, traffic data collection technologies have progressed fast. The emergence of innovative data collection technologies such as remote traffic microwave sensor, Bluetooth sensor, GPS-based floating car method, and automated license plate recognition, has significantly increased the variety and volume of traffic data. Despite the development of these technologies, the missing data issue is still a problem that poses great challenge for data based applications such as traffic forecasting, real-time incident detection, dynamic route guidance, and massive evacuation optimization. A thorough literature review suggests most current imputation models either focus on the temporal nature of the traffic data and fail to consider the spatial information of neighboring locations or assume the data follow a certain distribution. These two issues reduce the imputation accuracy and limit the use of the corresponding imputation methods respectively. As a result, this paper presents a Kriging based data imputation approach that is able to fully utilize the spatiotemporal correlation in the traffic data and that does not assume the data follow any distribution. A set of scenarios with different missing rates are used to evaluate the performance of the proposed method. The performance of the proposed method was compared with that of two other widely used methods, historical average and K-nearest neighborhood. Comparison results indicate that the proposed method has the highest imputation accuracy and is more flexible compared to other methods. [ABSTRACT FROM AUTHOR]

The decision making of travelers for route choice and departure time choice depends on the expected travel time and its reliability. A common understanding of reliability is that it is related to several statistical properties of the travel time distribution, especially to the standard deviation of the travel time and also to the skewness. For an important corridor in Changsha (P.R. China) the travel time reliability has been evaluated and a linear model is proposed for the relationship between travel time, standard deviation, skewness, and some other traffic characteristics. Statistical analysis is done for both simulation data from a delay distribution model and for real life data from automated number plate recognition (ANPR) cameras. ANPR data give unbiased travel time data, which is more representative than probe vehicles. The relationship between the mean travel time and its standard deviation is verified with an analytical model for travel time distributions as well as with the ANPR travel times. Average travel time and the standard deviation are linearly correlated for single links as well as corridors. Other influence factors are related to skewness and travel time standard deviations, such as vehicle density and degree of saturation. Skewness appears to be less well to explain from traffic characteristics than the standard deviation is. [ABSTRACT FROM AUTHOR]