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Intelligent traffic control and guidance system is an effective way to solve urban traffic congestion, improve road capacity and guarantee drivers' travel safety, while short-term traffic flow prediction is the core of intelligent traffic control and guidance system. To investigate the long-term memory and the dynamic feature of short-time traffic flow time series, a hybrid model was proposed by integrating autoregressive fractionally integrated moving average (ARFIMA) model and nonlinear autoregressive (NAR) neural network model to predict short-time traffic flow, in which ARFIMA model can address the long-term memory of linear component and NAR neural network can accommodate the dynamic feature of nonlinear residual component. First, the ARFIMA model was employed to predict the linear component of traffic flow, and the results were compared with those of autoregressive integrated moving average (ARIMA) model. Next, the NAR neural network model was adopted to forecast the nonlinear residual components, and the weighted results were considered as the predicted flow of the hybrid model. The proposed hybrid model was validated by using the cross-sectional traffic flow data in California freeways obtained from the open-access PeMS database. The results showed that the ARFIMA model considering the long-term memory can effectively predict the short-term traffic flow, and the prediction accuracy of the hybrid model is better than that of the singular models. The findings provide an alternative for the short-term traffic flow prediction with lower error and higher accuracy. [ABSTRACT FROM AUTHOR]

With the progress of urbanization and urban-rural integration (URI), the interaction between URI and urban-rural transport network is becoming stronger, thus it is necessary to investigate the interactive relationship between URI and transport network. By assuming certain functions and principles, evolution model was proposed to explain the relationship between URI space development scale and urban-rural transport network scale, and then by considering the interaction between URI and transport network, dynamic evolution model was presented. Stability analysis was performed to verify the interaction relations with three patterns and elasticity simulation provided the optimal value of urban-rural transport network scale configuration. The results may provide potential insights on URI development and transport network. [ABSTRACT FROM AUTHOR]

Although the pedestrian deaths have been declining in recent years, the pedestrian-vehicle death rate in Croatia is still pretty high. This study intended to explore the injury severity of pedestrian-vehicle crashes with panel mixed ordered probit model and identify the influencing factors at intersections. To achieve this objective, the data were collected from Ministry of the Interior, Republic of Croatia from 2015 to 2018. Compared to the equivalent random-effects and random parameter ordered probit models, the proposed model showed better performance on goodness-of-fit, while capturing the impact of exogenous variables to vary among the intersections, as well as accommodating the heterogeneity issue due to unobserved effects. Results revealed that the proposed model can be considered as an alternative to deal with the heterogeneity issue and to decide the factor determinants. The results may provide beneficial insight for reducing the injury severity of pedestrian-vehicle crashes. [ABSTRACT FROM AUTHOR]

This study intended to (1) investigate the pedestrian injury severity involved in traffic crashes; and (2) address the heterogeneity issue at signalized intersections. To achieve the objectives, Bayesian binary and ordinal quantile regression models were proposed to address the pedestrian injury severity at signalized intersections. The suitability of the proposed method was illustrated with the Hong Kong dataset from 2008 to 2012 and 376 signalized intersections involving 2090 pedestrian-related crashes are selected. It's found that age, injury location, pedestrian special circumstance, pedestrian contributory and the presence of Tram/LRT stops and right turning pocket are significant variables. The results indicated that both Bayesian binary and ordinal quantile regression models not only provide a more comprehensive and in-depth understanding of the relationship between pedestrian injury severity and the explanatory variables, but also highlight the heterogeneity issue for the data collected at different locations and different times without many assumptions. The goodness-of-fit of the proposed models outperforms existing mean models, while the Bayesian binary quantile model provides a better fit than the Bayesian quantile regression for ordinal model. The results can benefit the pedestrian facilities improvement/management and guide a much safer pedestrian environment. [ABSTRACT FROM AUTHOR]

The simultaneous estimation of crash frequency and severity has been studied for years, but most of the existing methodologies adopt mean regression models to estimate the parameters. This study presents the quantile selection model as a methodological alternative in analyzing crash rate and severity at different levels, focusing on addressing the heterogeneity and endogeneity issues so as to identify the influencing factors at signalized intersections. A two-step estimation procedure is carried out, in which the Heckman selection framework accommodates the endogenous relationship between crash rate and crash severity at different levels, while the quantile regression estimates various quantiles of crash rate instead of the mean regression, and accounts for the heterogeneity attributed to unobserved factors. Compare to the general Heckman selection model, the quantile approach is able to provide more comprehensive information about the impact of the influencing factors on crash rate. The model uses 555 observations from 262 signalized intersections in the Hong Kong metropolitan area, integrated with information on the traffic flow, geometric road design, road environment, traffic control and any crashes that occurred during two years. The proposed model reveals more detailed information in terms of different quantiles and improves the prediction accuracy. [ABSTRACT FROM AUTHOR]

• The pedestrian injury severity is investigated by GTWR model. • GWR model and TWR model are integrated to address the spatial and temporal heterogeneity, respectively. • The number of vehicles, number of casualties, speed limit, vehicle movement and injury location have significant influence in different areas. • The findings provide insights for practitioners and policy makers to improve pedestrian safety. This study intended to (1) investigate the pedestrian injury severity involved in traffic crashes; and (2) address the spatial and temporal heterogeneity simultaneously. To achieve the objectives, geographically and temporally weighted regression (GTWR) model was proposed to deal with both spatial and temporal heterogeneity simultaneously. The pedestrian crash data of Hong Kong metropolitan area from 2008 to 2012 were collected, involving 1652 pedestrian-related injury samples. By comparing GTWR model and standard geographically weighted regression (GWR) model and temporally weighted regression (TWR) model, the proposed GTWR model showed potential benefits in modeling both spatial and temporal non-stationarity simultaneously in terms of goodness-of-fit and F statistics. Results revealed that number of vehicles, number of pedestrian-related casualties, speed limit, vehicle movement and injury location have significant influence on pedestrian injury severity in different areas. The conclusions are reached that GRWR model can address the relationship between pedestrian injury severities and influencing factors, as well as accommodating spatial and temporal heterogeneity simultaneously. The findings provide useful insights for practitioners and policy makers to improve pedestrian safety. [ABSTRACT FROM AUTHOR]

Urban expressway is the main artery of traffic network, and an in-depth analysis of the crashes is crucial for improving the traffic safety level of expressways. This study intended to address the injury severity of expressways in Beijing by proposing Bayesian ordered logistic regression model. Crash data were collected from urban express rings and expressways in 2015 and 2016. The results showed that crash location, time and crash season are significant variables influencing injury severity. The findings revealed that the proposed model can address the ordinal feature of injury severity, while accommodating the data with small sample sizes that may not adequately represent population characteristics. The conclusions can provide the management departments with valuable suggestions for the injury prevention and safety improvement on the urban expressways. [ABSTRACT FROM AUTHOR]

This study is intended to investigate the influencing factors of injury severity by considering the heterogeneity issue of unobserved factors at different arterials and the spatial attributes in geographically weighted regression models. To achieve the objectives, geographically weighted panel logistic regression model was developed, in which the geographically weighted logistic regression model addressed the injury severity from the spatial perspective, while the panel data model accommodated the heterogeneity attributed to unobserved factors from the temporal perspective. The geo-crash data of Las Vegas metropolitan area from 2014 to 2016 was collected, involving 27 arterials with 25,029 injury samples. By comparing the conventional logistic regression model and geographically weighted logistic regression models, the geographically weighted panel logistic regression model showed preference to the other models. Results revealed that four main factors, human-beings (drivers/pedestrians/cyclists), vehicles, roadway, and environment, were potentially significant factors of increasing the injury severity. The findings provide useful insights for practitioners and policy makers to improve safety along arterials. [ABSTRACT FROM AUTHOR]

Urban-rural integration is the critical process of urbanization, while during this process there are various corresponding problems generated, safety is the most important one, especially at urban-rural fringe areas due to mixed traffic flow and complicated land use. This study intended to investigate the injury severity interaction between urban area and urban-rural fringe area, and explore the correlation within injury severity levels and heterogeneity attributed to unobserved factors. To address the correlation and heterogeneity issues, a grouped random parameters seemingly unrelated bivariate (SUB) probit model was proposed, in which the SUB probit model addressed the correlation of residuals, while the random parameters model accommodated the heterogeneity due to unobserved factors. By comparing the pooled with random parameters models, the results showed that random parameters SUB probit model performed better than the pooled one, and the dataset collected from 2013 to 2017 in Chengdu, China was adopted to illustrate the proposed model. It is found that crash location, speed limit and age of person injured are significant for injury severity in urban and urban-rural fringe areas, but crash form plays a significant role in urban area while number of persons involved should be paid more attention due to injury severity in urban-rural fringe area. Some empirical suggestions are presented to improve the safety in urban and urban-rural fringe areas. [ABSTRACT FROM AUTHOR]

Highlights • The interactions between accident severity levels and traffic signs are identified in state roads. • A seemingly unrelated regression (SUR) model in unbalanced panel data approach is proposed. • The seemingly. unrelated regression model addresses the correlation of residuals between different accident severity levels, and the panel data model accommodates the heterogeneity attributed to unobserved factors. • By comparing the pooled, fixed-effects and random-effects SUR models, the random-effects SUR model shows priority to the other two. • The results reveal that different traffic signs cause different accident severity levels, which benefits the policy makers and roadway management departments. Abstract This study intended to investigate the interactions between accident severity levels and traffic signs in state roads located in Croatia, and explore the correlation within accident severity levels and heterogeneity attributed to unobserved factors. The data from 410 state roads between 2012 and 2016 were collected from Traffic Accident Database System maintained by the Republic of Croatia Ministry of the Interior. To address the correlation and heterogeneity, a seemingly unrelated regression (SUR) model in unbalanced panel data approach was proposed, in which the seemingly unrelated model addressed the correlation of residuals, while the panel data model accommodated the heterogeneity due to unobserved factors. By comparing the pooled, fixed-effects and random-effects SUR models, the random-effects SUR model showed priority to the other two. Results revealed that (1) low visibility and the number of invalid traffic signs per km increased the accident rate of material damage, death or injured; (2) average speed limit exhibited a high accident rate of death or injured; (3) the number of mandatory signs was more likely to reduce the accident rate of material damage, while the number of warning signs was significant for accident rate of death or injured. [ABSTRACT FROM AUTHOR]

This study intended to investigate the interactions between accident severity levels and traffic signs in state roads located in Croatia and explore the correlation between accident severity levels and heterogeneity attributed to unobserved factors. The data from 460 state roads between 2012 and 2016 were collected from Traffic Accident Database System maintained by the Republic of Croatia Ministry of the Interior. To address the correlation and heterogeneity, Bayesian bivariate Tobit quantile regression models were proposed, in which the bivariate framework addressed the correlation of residuals with Bayesian approach, while the Tobit quantile regression model accommodated the heterogeneity due to unobserved factors. By comparing the Bayesian bivariate Tobit quantile and mean regression models, the proposed quantile models showed priority to mean model. Results revealed that (1) low visibility and the number of invalid traffic signs per km increased the accident rate of material damage, death, or injury; (2) average speed limit exhibited a close relation with accident rate; and (3) the number of mandatory signs was more likely to reduce the accident rate of material damage, while the number of warning signs was significant for accident rate of death or injury. [ABSTRACT FROM AUTHOR]

Purpose: The objective of this paper is to provide a new method for estimating crash rate and severity simultaneously. Methods: This study explores a Heckman selection model of the crash rate and severity simultaneously at different levels and a two-step procedure is used to investigate the crash rate and severity levels. The first step uses a probit regression model to determine the sample selection process, and the second step develops a multiple regression model to simultaneously evaluate the crash rate and severity for slight injury/kill or serious injury (KSI), respectively. The model uses 555 observations from 262 signalized intersections in the Hong Kong metropolitan area, integrated with information on the traffic flow, geometric road design, road environment, traffic control and any crashes that occurred during two years. Results: The results of the proposed two-step Heckman selection model illustrate the necessity of different crash rates for different crash severity levels. Conclusions: A comparison with the existing approaches suggests that the Heckman selection model offers an efficient and convenient alternative method for evaluating the safety performance at signalized intersections. [ABSTRACT FROM AUTHOR]

This study investigated the level of injury severity in crashes in right-turn lanes at signalised intersections. It used a dataset of 1900 injuries occurring at 275 signalised intersections in the Las Vegas, NV, USA area in the period 2003 to 2005. An advanced random-parameter binary logit model was used to determine the factors that significantly influence injury severity - categorised as either property damage only or severe injury - in right-turn lanes. A comparison of this model with the traditional fixed-parameter model was made to account for the unobserved heterogeneity. It was comparable to the two-level binary logistic model, which accounts for cross-group heterogeneity. The analysis showed that the following factors lead to a significantly higher likelihood of severe injury: rear-end crashes, the involvement of a vehicle going through the intersection, stopped and parked vehicles on main and minor streets, length of corner clearance, number of through lanes on minor streets and intersection angle. [ABSTRACT FROM AUTHOR]

The present study intended to (1) investigate the injury risk of pedestrian casualties involved in traffic crashes at signalized intersections in Hong Kong; (2) determine the effect of pedestrian volumes on the severity levels of pedestrian injuries; and (3) explore the role of spatial correlation in econometric crash-severity models. The data from 1889 pedestrian-related crashes at 318 signalized intersections between 2008 and 2012 were elaborately collected from the Traffic Accident Database System maintained by the Hong Kong Transport Department. To account for the cross-intersection heterogeneity, a Bayesian hierarchical logit model with uncorrelated and spatially correlated random effects was developed. An intrinsic conditional autoregressive prior was specified for the spatial correlation term. Results revealed that (1) signalized intersections with greater pedestrian volumes generally exhibited a lower injury risk; (2) ignoring the spatial correlation potentially results in reduced model goodness-of-fit, an underestimation of variability and standard error of parameter estimates, as well as inconsistent, biased, and erroneous inference; (3) special attention should be paid to the following factors, which led to a significantly higher probability of pedestrians being killed or sustaining severe injury: pedestrian age greater than 65 years, casualties with head injuries, crashes that occurred on footpaths that were not obstructed/overcrowded, heedless or inattentive crossing, crashes on the two-way carriageway, and those that occurred near tram or light-rail transit stops. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

To investigate pedestrian exposure when only pedestrian-crash data are available, the quasi-induced exposure method is used to identify the factors that contributed to pedestrian-vehicle crashes in Las Vegas from 2004 to 2008. The results show that overall crash severity, light conditions, and weather conditions are potentially risky factors in pedestrian crashes, and the time of day or day of the week are less risky. Multiple-correspondence analysis (MCA) is then used to investigate how pedestrian crash severity is influenced by the interactions among a range of variables. The results indicate that among instances of pedestrian-vehicle crashes cases of property damage only tend to occur during off-peak daytime hours and under dark or dawn light conditions in clear weather. Crashes resulting in injuries usually occur during weekday peak hours in daylight and under rainy conditions. Most fatal crashes occur during off-peak hours, at night on the weekends. [ABSTRACT FROM AUTHOR]

The objective of this study was to identify the influencing factors of road traffic crash rates from the perspective of access management techniques in urban areas. The target areas are located in the Las Vegas metropolitan area in the USA and 19 arterial routes were selected. To address the interdependency between crash rates and travel speeds and left-censored issue, simultaneous equation Tobit models are presented. The structure of the model addresses the left-censored issue for the segments while the simultaneous equation models treat the endogeneity issue between crash rates and travel speeds. The results indicate that there is a strong interdependency between crash rates and travel speeds. Segment length, driveway density, median (central reservation) opening density, posted speed limit and annual average daily traffic per lane were found to be statistically significant factors that influence both crash rates and travel speeds on segments. Moreover, crash rates are significantly influenced by two-directional median opening density. [ABSTRACT FROM AUTHOR]

In recent years the pedestrian deaths have been declining, but the pedestrian–vehicle death rate in Croatia is still pretty high. This study intended to investigate the injury severity of pedestrian–vehicle crashes and identify the influencing factors. To achieve this goal, the dataset was firstly collected from Traffic Accident Database System maintained by the Ministry of the Interior, Republic of Croatia from 2015 to 2019, and then latent cluster analysis was employed to identify homogenous clusters from heterogeneous dataset. Based on the classified dataset, unbalanced panel mixed ordered probit model was proposed. By analyzing the classes with different vehicles, the proposed model revealed a more complete understanding of significant variables and showed beneficial performance from the goodness-of-fit, while capturing the impact of exogenous variables to vary among different places, as well as accommodating the heterogeneity issue due to unobserved effects. Findings revealed that the proposed model can be considered as an alternative to determine the factors of injury severity and to deal with the heterogeneity issue. The results may provide potential insights for reducing the injury severity of pedestrian-vehicle crashes. [ABSTRACT FROM AUTHOR]

• The impact of influencing factors on injury severity of side crashes at intersections and role of striking and struck vehicles are investigated. • Bayesian bivariate probit model is proposed to accommodate the correlation between the injury severity levels of striking and struck vehicles, and Bayesian approach via Markov Chain Monte Carlo (MCMC) is employed to estimate the model. • The Bayesian bivariate probit model can effectively address the correlation and provide better model fit than Bayesian univariate probit model. • The findings can help designers and management departments improve the safety at intersections. Side crashes between vehicles which usually lead to high casualties and property loss, rank first among total crashes in China. This paper aims to identify the factors associated with injury severity of side crashes at intersections and to provide suggestions for developing countermeasures to mitigate the levels of injuries. In order to investigate the role of striking and struck vehicles in side crashes simultaneously, bivariate probit model was proposed and Bayesian approach was employed to evaluate the model, compared to the corresponding univariate probit model. Crash data from Beijing, China for the period 2009–2012 were used to carry out the statistical analysis. Based on the investigation with vehicles and data analysis on events, 130 intersection side crash cases were selected to form a specific dataset. Then, the influence of human, vehicles, roadway and environmental variables on crash severity was examined by means of bivariate probit regression within Bayesian framework. The effects of the factors on striking vehicle drivers and struck vehicle drivers were considered separately and simultaneously to find more targeted conclusions. The statistical analysis revealed vehicle type, lane number, no non-motorized lane and speeding have the corresponding influence on the injury severity of striking vehicles, while time of day and vehicle type of struck vehicles increased the likelihood of being injured. From the results it can be concluded that there indeed exists correlation between striking and struck vehicles in side crashes, although the correlation is not so strong. Importantly, Bayesian bivariate probit model can address the role of striking and struck vehicles in side crashes simultaneously and can accommodate the correlation clearly, which extends the range of univariate probit analysis. The general and empirical countermeasures are presented to improve the safety at intersections. [ABSTRACT FROM AUTHOR]

To comprehensively understand the research progress of the fleet scheduling for hazardous materials, the study has summarized the corresponding research results from three aspects (a) hazardous materials transportation risk, (b) route optimization, and (c) fleet scheduling, and then pointed out potential problems from six aspects: (a) the coupling risk of the transport fleet; (b) the screening of time and space for the transport of hazardous materials; (c) the scheduling optimization for transport fleets; (d) taking insufficient account of transport risks fairness; (e) insufficient robustness of scheduling schemes; and (f) lacking of research results on fleet scheduling of transport in the context of antiterrorism. After that, by considering the existing shortcomings of the current research, five research directions are presented that should be further explored in the future. Subsequently, both rough set and association rule theory is applied to explore the cause chain of transportation accidents for hazardous materials, and analyze the mechanism of transport accident for hazardous materials. Next, the Bayesian network is presented to predict the accident rate of hazardous materials transportation under different temporal and spatial conditions, and the dynamic rolling scheduling method of hazardous materials transport fleet is constructed under normal and antiterrorism background. [ABSTRACT FROM AUTHOR]

To understand the status quo of urban recurrent traffic congestion, the current results of recurrent traffic congestion, and gating control are reviewed from three aspects: traffic congestion identification, evolution trend prediction, and urban road network gating control. Three aspects of current research are highlighted: (a) The majority of current studies are based on statistical analyses of historical data, while congestion identification is performed by acquiring small-scale traffic parameters. Thus, congestion studies on the urban global roadway network are lacking. Situation identification and the failure to effectively warn or even avoid traffic congestion before congestion forms are not addressed; (b) correlation studies on urban roadway network congestion are inadequate, especially regarding deep learning, and considering the space-time correlation for congestion evolution trend prediction; and (c) quantitative research methods, dynamic determination of gating control areas, and effective countermeasures to eliminate traffic congestion are lacking. Regarding the shortcomings of current studies, six research directions that can be further explored in the future are presented. [ABSTRACT FROM AUTHOR]

In order to effectively improve the capacity of expressways under adverse weather conditions (rainy, foggy and snowy) and reduce the potential safety hazards of expressways, a variable speed limit control strategy is proposed. First, the impact of road alignment on traffic capacity is considered, and road traffic capacity reduction coefficients under different bends and slopes are added so that the cell transmission model and a dynamic traffic flow model can be established; Secondly, the maximum speed limit value under adverse weather conditions is determined according to the influence of rainfall intensity under rainy weather conditions, visibility under foggy weather conditions, and road adhesion coefficient under snowy weather conditions on traffic capacity; Finally, an adaptive genetic algorithm is employed to solve the function that satisfies the safety and efficiency of traffic operation. The results show that the variable speed limit control strategy under adverse weather conditions effectively improves the road capacity, reduces the driving risk of vehicles, and alleviates traffic congestion. [ABSTRACT FROM AUTHOR]

This study intended to investigate the crash injury severity from the insights of the novice and experienced drivers. To achieve this objective, a bivariate panel data probit model was initially proposed to account for the correlation between both time-specific and individual-specific error terms. The geocrash data of Las Vegas metropolitan area from 2014 to 2017 were collected. In order to estimate two (seemingly unrelated) nonlinear processes and to control for interrelations between the unobservables, the bivariate random-effects probit model was built up, in which injury severity levels of novice and experienced drivers were addressed by bivariate (seemingly unrelated) probit simultaneously, and the interrelations between the unobservables (i.e., heterogeneity issue) were accommodated by bivariate random-effects model. Results revealed that crash types, vehicle types of minor responsibility, pedestrians, and motorcyclists were potentially significant factors of injury severity for novice drivers, while crash types, driver condition of minor responsibility, first harm, and highway factor were significant for experienced drivers. The findings provide useful insights for practitioners to improve traffic safety levels of novice and experienced drivers. [ABSTRACT FROM AUTHOR]

This study intended to investigate the crash injury severity from the insights of the novice and experienced drivers. To achieve this objective, a bivariate panel data probit model was initially proposed to account for the correlation between both time-specific and individual-specific error terms. The geocrash data of Las Vegas metropolitan area from 2014 to 2017 were collected. In order to estimate two (seemingly unrelated) nonlinear processes and to control for interrelations between the unobservables, the bivariate random-effects probit model was built up, in which injury severity levels of novice and experienced drivers were addressed by bivariate (seemingly unrelated) probit simultaneously, and the interrelations between the unobservables (i.e., heterogeneity issue) were accommodated by bivariate random-effects model. Results revealed that crash types, vehicle types of minor responsibility, pedestrians, and motorcyclists were potentially significant factors of injury severity for novice drivers, while crash types, driver condition of minor responsibility, first harm, and highway factor were significant for experienced drivers. The findings provide useful insights for practitioners to improve traffic safety levels of novice and experienced drivers. [ABSTRACT FROM AUTHOR]

Customized public transit, as a diversified passenger service mode proposed under the new situation, not only deals with the comfortable and convenient commuting needs of most commuters, but also improves the efficiency of road use effectively and alleviates the road traffic pressure in the morning and evening rush hours. Currently, various routing optimization methods based on static passenger requests have been applied to the optimization design of customized bus routing in major cities, but the static passenger requests cannot satisfy the dynamic passenger requests of random users at present Therefore, it is urgent and necessary to investigate real-time customized bus routing optimization methods based on static passenger requests and dynamic passenger requests. In this paper, our goal is to present an approach to tackle two important issues of real-time customized bus route optimization under stochastic user demand: one is that how to deal with dynamic travel requests in a specific road network to improve passengers' satisfaction with customized bus services; the other is that how to employ specific methods to achieve real-time updating of customized bus routes to meet the random user's travel requests. Starting from the whole operation process of customized bus, we firstly construct a multi-objective optimization model of customized bus route, and clearly depicts the four processes of customized bus operation in the form of mathematical expressions. Second, we design a two-stage method based on NSGA-II algorithm to solve the model, in which the first stage deals with static bus travel requests to obtain the initial customized bus route optimization scheme, and the second stage deals with dynamic bus travel requests to update the customized bus route in real time. Finally, a two-stage case is illustrated to verify the correctness and validity of the model and the method for solving the real-time customized bus route optimization problem under stochastic user demand. • Propose a multi-objective optimization model for customized bus routes under stochastic demand. • Consider the real-time customized bus route optimization integrated static and dynamic ride request. • Employ a two-stage method based on NSGA-II algorithm to process static and dynamic ride request. • Adopt the three-stage hybrid coding to deal with the whole chromosome segment. [ABSTRACT FROM AUTHOR]