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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.

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 [...]

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 [...]