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With the continuous development of the COVID-19 pandemic, the selection of locations for medical isolation areas has not always been optimal for the timely transportation of infected people, or those suspected of being infected. This has resulted in failure to control the rate of spread of infection cases in time. To address this problem, this paper proposes a co-evolutionary location-routing optimization (CELRO) model of medical isolation areas for use in major public health emergencies to develop a rapid location-routing scheme for epidemic isolation, including the selection of locations of medical isolation facilities per area and the optimal route per vehicle to each infected person. Specifically, this paper solves the following two sub-problems: (i) calculate the shortest transportation times and corresponding routes from any medical isolation area to any person infected or suspected of being infected, and (ii) calculate the location scheme for distribution of isolation areas. Different from previous studies, the vehicle operating characteristics and the interference of uncertainty of the traffic environment are considered in the proposed model. To find an appropriate scheme for location of medical isolation areas with the shortest travel times, a co-evolutionary clustering algorithm (CECA), which is a combination of some separated evolutionary programming operations, is proposed to solve the model. Various network sizes and uncertainty combinations are used to design some comparative tests, which aim to verify the effectiveness of the proposed model. In the experiment section, CELRO reduced travel time by at least 14% compared with other methods. This finding can provide an effective theoretical basis for optimizing the spatial layout of medical isolation areas or the location planning of new medical facilities.

The paper proposes a model of mandatory lane-changing behaviour based on a non-cooperative game in a traditional environment and analyses its applicability in a connected environment. In order to solve the problem of traffic safety and traffic congestion caused by mandatory lane-changing on urban roads, this paper applies the non-cooperative game theory to describe the game behaviour of the two parties, the lane-changing vehicle and the vehicle behind the target lane, in the connected and traditional environments respectively, and constructs the model considering the safety gain, speed gain and lane-changing gain to obtain a game model and the Nash equilibrium solution. The model is calibrated and tested using NGSIM data, and the results of the study show that the model has a good performance for the decision behaviour of lane-changing vehicles and lag vehicles for mandatory lane-changing behaviour on urban roads.

This paper develops a vehicle scheduling method for the electric bus (EB) route considering stochastic volatilities in trip travel time and energy consumption. First, a model for estimating the trip energy consumption is proposed based on field-collected data, and the probability distribution function of trip energy consumption considering the stochastic volatility is determined. Second, we propose the charging strategy to recharge buses during their idle times. The impacts of stochastic volatilities on the departure time, the idle time, the battery state of charge, and the energy consumption of each trip are analyzed. Third, an optimization model is built with the objectives of minimizing the expectation of delays in trip departure times, the summation of energy consumption expectations, and bus procurement costs. Finally, a real bus route is taken as an example to validate the proposed method. Results show that reasonable idle times can be generated by optimizing the scheduling plan, and it is helpful to stop the accumulation of stochastic volatilities. Collaboratively optimizing vehicle scheduling and charging plans can reduce the EB fleet and delay times while meeting the route operation needs.

Purpose The purpose of this paper is to develop a dynamic control method to improve bus schedule adherence under connected bus system. Design/methodology/approach The authors developed a dynamic programming model that optimally schedules the bus operating speed at road sections and multiple signal timing plans at intersections to improve bus schedule adherence. First, the bus route was partitioned into three types of sections: stop, road and intersection. Then, transit agencies can control buses in real time based on all collected information; i.e. control bus operating speed on road sections and adjust the signal timing plans through signal controllers to improve the schedule adherence in connected bus environment. Finally, bus punctuality at the downstream stop and the saturation degree deviations of intersections were selected as the evaluation criteria in optimizing signal control plans and bus speeds jointly. Findings An illustrative case study by using a bus rapid transit line in Jinan city was performed to verify the proposed model. It revealed that based on the proposed strategy, the objective value could be reduced by 73.7%, which indicated that the punctuality was highly improved but not to incur excessive congestion for other vehicular traffic. Originality/value In this paper, the authors applied speed guidance and the adjustment of the signal control plans for multiple cycles in advance to improve the scheduled stability; furthermore, the proposed control strategy can reduce the effect on private traffics to the utmost extend.

Since bus inter-stop travel times (BISTTs) are significant components of the route travel time, providing accurate BISTTs can effectively improve the route travel time prediction accuracy. In this ...

Bus routes overlapping would lead to more than one bus entering the stop simultaneously, which may trigger bus bunching. Focusing on high frequency routes with common stops, this paper proposes a mixed scheduling method combining the all-stop service and the stop-skipping service. The method optimizes scheduling strategies for multiple routes by minimizing total passenger travel time. The optimization variables are binary variables reflecting whether the stops in the overlapping area are skipped. Three exciting bus routes are employed for case study. Results show that the proposed method reduces total passenger travel time by 21.4% compared with the current scheduling strategy.

To mitigate the problems caused by bus route overlap in the transit network, this paper proposes a new scheduling method with both large and small vehicle types based on passenger OD (Origin-Destination) data. The minimum of total cost of passenger travel time and bus company operation is taken as the optimization objective, departure intervals and vehicle types are taken as the optimization variables. The impact of route overlap on passenger travel time is analyzed. A heuristic algorithm is developed to solve the optimization model to produce the departure time and vehicle type for each bus trip. Finally, three real bus routes in Harbin city are taken as an example to validate the proposed model using peak-hour data. Compared with the model without considering route overlap, the proposed model can reduce total passenger travel time and cost by 5.2% and 8.8% respectively.

Improving lighting arrangements at the highway intersections can significantly reduce the likelihood of crashes. Meanwhile, a reasonable gradient illumination scheme can increase drivers' safety by avoiding the rapid change of their pupil area when driving from an unlighted area into a lighted area. The purpose of this study is to design illumination transition zones for drivers when approaching the highway intersection, and optimize the illumination increments along the transition zones. This issue is addressed in three stages. First, an indoor simulation platform is built using the UC-win/road software, and its parameters are calibrated using real intersection data collected at night. Second, the variation of pupil area under different increasing rates of illuminance near the driver's eyes (INDE) is analyzed, and a model representing the temporal change of the area in the pupil contraction stage is established. Last, an optimization model is proposed to obtain the optimal luminance increasing rates by minimizing the time needed for drivers to travel through the transition zones under different maximum illuminations and speed limits. The findings of this study indicate that the INDE increasing time in the transition zone remains unchanged with a determined illumination at the intersection, and the optimal transition zone length is directly proportional to the speed limit of the intersection. Therefore, the transition zone length needs to be adjusted to accommodate different speed limits: it should be extended or shortened by the same percentage as the increase or decrease of the speed limit. It can be concluded that the illumination transition zone is necessary when the INDE of highway intersection and the speed limit exceed 8 lx and 40 km/h, respectively. The INDE increasing rate should be maintained around the optimal value, providing safe and comfortable light adaptation to drivers and preventing traffic accidents. The study will provide a scientific basis for safety implementation of lighting arrangements at highway intersections.

Lane-changing trajectory planning (LTP) is an effective concept to control automated vehicles (AVs) in mixed traffic, which can reduce traffic conflicts and improve overall traffic efficiency. To enhance the lane change safety for AVs, a co-evolutionary lane-changing trajectory planning (CLTP) method is proposed to describe the risk minimization process that co-evolves with the dynamic traffic environment in the limited literature. Firstly, the natural driving data of vehicle trajectory on the expressway provided by the High dataset are used to construct the lane-changing samples. To obtain the future traffic environment information, a deep learning neural network is adopted to capture trajectory dynamics in mobility of surrounding vehicles around a lane-changing vehicle. Secondly, the safe interaction between the subject vehicle and the surrounding vehicles is considered to establish a mathematical model for the temporal and spatial risk identification of a lane change event based on the fault tree analysis method. Subsequently, the risk minimization of lane change is considered as the objective. Based on the acceleration and deceleration overtaking rules and the trapezoidal acceleration method, the longitudinal and lateral displacement schemes during a lane change are designed. Finally, the motion parameters of longitudinal and lateral displacement are acquired to form an ideal lane change trajectory using a genetic algorithm. The results show that this method can effectively achieve higher safety of the lane-changing process, and reduce the traffic conflicts and traffic turbulence caused by dangerous lane-changing behaviors. The findings can provide theoretical support for lane change trajectory planning algorithm design of intelligent vehicles.

Bus route clustering problem (BRCP) concerns the assignment of bus routes to different boarding locations of a bus station with the objective of minimizing passenger waiting time. In this study, we formulate the BRCP as a mixed-integer second-order cone program (MISOCP). Simulations are conducted, in which the MISOCP model is applied to a major bus station in Hong Kong based on the network of actual bus routes. Experiments are tested for large size instances under different scenarios. Results show that the complexity of the BRCP is highly dependent on the overlapping degree of bus networks, while other factors, including the number of bus routes, destinations, and boarding locations have a joint effect; the influence is instance-specific based on different overlapping topologies of bus route networks.

Computer-Aided Civil and Infrastructure Engineering To prevent bus bunching, a dynamic headway control method in the V2I (vehicle to infrastructure) environment for a high-frequency route with bus lane is developed. Bus operating speed guidance on the mid-blocks and intersection signal adjustment are two main strategies in the proposed method. A forecasting model of bus travel time under the dynamic control method is developed. The objective function is set up by taking into account differences between actual bus headways and dispatching headways, and the scaling ratios of intersection cycle lengths. The optimization model is solved using genetic algorithm. The proposed method is applied to a real bus route in Meihekou city, China, and compared with the current control plan as well as holding strategy. Results show that the proposed method can reduce bus headway deviations in all investigating periods; negative impacts on cars can be limited by setting reasonable values for the parameters.

The practical implementations of congestion-pricing are largely restricted, due to the low public acceptance level. Based on a field survey, this study reveals the public acceptance level in Melbourne, Australia. It was found that the level of acceptance for a new congestion-pricing scheme is 42%, which still needs to be improved if a congestion-pricing scheme is to be implemented. Some strategies are proposed and discussed to increase the acceptance level towards congestion charge in urban cities, including an information campaign, public transport improvements and a trial. First Published Online:29 Mar 2016

Based on the fact that there are nonstrict priority left-turning maneuvers at signalized intersections with a permitted phase in some countries, this study proposes a left-turn unit-based a...

Electric buses (EBs) have the characteristics of zero emission and low noise, which plays an important role in reducing air and noise pollution and improving air quality in metropolitan areas. Choosing the appropriate electric buses type to meet the passenger demand and own interests is an important practical problem faced by many bus companies, among many types of electric buses. This paper conducts an optimization study on the vehicle and charger performance parameters selection for electric bus routes, with the objectives of minimizing the total operating cost of the bus company, including the annual average electric bus purchase cost, annual average charging facilities cost, and annual charging cost of electric bus fleet, and considers the passenger travel demand and integrity of bus service. Finally, two real electric bus routes are taken as an example to validate the proposed method. Results show that the optimized scheme is more conducive to saving the operation cost of bus companies compared with the current scheme.

Electric buses are more environment-friendly due to their low noise and less air pollution. However, their electricity consumption on the route will change with operating conditions. According to field investigations, ambient temperature is one main contributing factor to energy consumption of an electric bus. When the temperature is very low, the energy consumption would increase significantly. The operational performance of the electric bus in cold regions should be examined carefully based on real world operation data. Thus, we choose Meihekou city, China which belongs to cold regions to collect ambient temperature and corresponding electricity consumption for six buses on a bus line. We gathered ambient temperature and corresponding electricity consumption of a trip in one day for six buses around a year to test the relationship between them. Pearson Correlation Coefficient is applied to verify the relevance of ambient temperature and electricity consumption. Results prove a negative correlation between them. After that, temperature and corresponding electricity consumption of the whole day for a year are studied. Ultimately, results illustrate electricity consumption variation is diverse during different seasons, and the largest electricity consumption is in winter. Results also show that when ambient temperature range drops from [−2, 3] to [−10, −2], change of electricity consumption is unstable and violent, which rises from 0.45 to 0.7 kWh/km. However, when ambient temperature ranges from −10 to −25.5 °C, the fluctuation of electricity consumption is small, which is dispersed between 0.6 and 0.7 kWh/km.

Bus signal priority (BSP) is an effective strategy to enhance the quality of public transit service, but unfortunately, it also deteriorates the performance of general vehicles. However, th...

The promotion of electric buses is of great significance for reducing vehicle emission, decreasing operation costs of transit corporations and workloads of bus drivers. However, the adoption of electric buses is constrained by their limited driving range. To guarantee the regular level of service, electric buses need to get recharged during daily operating hours. Electric bus battery life is highly correlated to charging modes. In this study, we proposed a mixed charging strategy with the setup of lower and upper limits of battery state of charge (SOC). A bi-level optimization model for electric bus scheduling was developed considering bus fleet size, variance of travel times of all buses and their idling times. The lower-level model is to minimize the variance of travel times and to maximize the average idling times of all buses. The upper-level model is to minimize the extra economic cost resulting from the bus fleet expansion. A case study is conducted to assess the proposed optimization model with a real electric bus route. The results show that the proposed model is capable of maintaining bus battery SOC within the reasonable range.

Bus holding strategy is an effective way for alleviating inevitable disruptions along a bus route, including decisions on the control point locations and slack duration. However, at present, the locations of the control points and corresponding slack times are largely determined by engineering experience and therefore lack theoretical optimization. The aim of this study is to develop a model which can jointly optimize the locations of the control points and corresponding slack times under a given number of control points. As locations of the upstream control points will influence the downstream operation states, this optimization problem is modeled as a multi-stage decision process. To evaluate the stability and efficiency of a bus system, we calculate the punctuality rate and travel time at each stage by taking into account the uncertainty along the route and propose a reasonable stage-utilization cost combing these two factors. A numerical case study based on a real bus route in Harbin of China is carried out to demonstrate our approach. The impacts of the weighting factor and slack times on the optimal control point plan are also discussed.

The spatial–temporal accessibility of a transport system assesses the spatial–temporal constraints faced by individuals based on their fixed activities and the ability of the transport system to facilitate trading time for space in movement. Previous studies either measured the individual spatial–temporal accessibility of a general transport network or measured the cumulative-opportunity accessibility by public transit through an exclusive computation of the bottom of the full network time prism. By contrast, the current study measures the individual spatial–temporal accessibility by public transit by integrating timetable and passenger departure time and computing the full network time prism. A public transit network is modelled as a time-dependent weighted directed graph, wherein every single directed arc is associated with a time-dependent travel time to represent the linkage between two adjacent stops on a transit route. The time-dependent travel time assigned to arcs is determined according to timetables, which is particularly assigned to infinity when transit service is unavailable between two stops. A modified network potential path area (N-PPA) algorithm based on the time-dependent weighted directed graph is employed to produce a potential path area for the activity participation of an individual by public transit. The proposed methodology is applied as a case study to measure individual spatial–temporal accessibility using the Salt Lake City TRAX system. Results indicate that the outcome of the proposed methodology is sensitive to departure time of passengers. The results of this study provide suggestions on potential improvement of bus/rail line layout and timetables and may aid in trip planning of passengers.

This paper is a case study to estimate the Saturation Flow Rate (SFR) on a practical shared left-turn lane during peak hour and off-peak hour using KPSS test method and Highway Capacity Manual (HCM) method. The results declare that the value of SFR estimated by HCM is less than the one estimated by KPSS test method during all time periods. The difference is 4.1% during off-peak hour while 1.2% during peak hour. However, this slight discrepancy could cause remarkable difference in cycle length and average vehicle delay. For example, compared with KPSS test method, in off-peak hour the cycle length increases by 19.2% while the average vehicle delay increases by 14.0% when using HCM method.

Saturation flow rate (SFR) estimation of approaching lanes is an essential task to evaluate the signal timing and level of service of signalized intersections. There are few existing studies on SFR estimation of the shared left-turn lane. According to relative differences of directions between the front and behind vehicles, this paper divides the headways of shared left-turn lane during green times into four categories. For each category, a method is raised to identify whether the headways are saturated based on the KPSS test. The paper first provides a method to eliminate outliers considering the factors such as distracted driving and stochastic flow. The estimated value of SFR should be 3600 divided by saturation headway, and the estimated value of saturation headway is calculated as the weighted average value of four categories.

Right-turn motorized vehicles turn right using channelized islands, which are used to improve the capacity of intersections. For ease of description, these kinds of right-turn motorized vehicles are called advance right-turn motorized vehicles (ARTMVs) in this paper. The authors analyzed four aspects of traffic conflict involving ARTMVs with other forms of traffic flow. A capacity model of ARTMVs is presented here using shockwave theory and gap acceptance theory. The proposed capacity model was validated by comparison to the results of the observations based on data collected at a single intersection with channelized islands in Kunming, the Highway Capacity Manual (HCM) model and the VISSIM simulation model. To facilitate engineering applications, the relationship describing the capacity of the ARTMVs with reference to the distance between the conflict zone and the stop line and the relationship describing the capacity of the ARTMVs with reference to the effective red time of the nonmotorized vehicles moving in the same direction were analyzed. The authors compared these results to the capacity of no advance right-turn motorized vehicles (NARTMVs). The results show that the capacity of the ARTMVs is more sensitive to the changes in the arrival rate of nonmotorized vehicles when the arrival rate of the nonmotorized vehicles is 500 (veh/h)~2000 (veh/h) than when the arrival rate is some other value. In addition, the capacity of NARTMVs is greater than the capacity of ARTMVs when the nonmotorized vehicles have a higher arrival rate.

Countdown timers display the time left on the current signal, which makes drivers be more ready to react to the phase change. However, previous related studies have rarely explored the effects of countdown timer on driver's brake perception-reaction time (BPRT) to yellow light. The goal of this study was therefore to characterize and model driver's BPRT to yellow signal at signalized intersections with and without countdown timer. BPRT data for "first-to-stop" vehicles after yellow onset within the transitional zone were collected through on-site observation at six signalized intersections in Harbin, China. Statistical analysis showed that the observed 15th, 50th, and 85th percentile BPRTs without countdown timer were 0.52, 0.84, and 1.26s, respectively. The observed 15th, 50th, and 85th percentile BPRTs with countdown timer were 0.32, 1.20, and 2.52s, respectively. Log-logistic distribution appeared to best fit the BPRT without countdown timer, while Weibull distribution seemed to best fit the BPRT with countdown timer. After that, a Log-logistic accelerated failure time (AFT) duration model was developed to model driver's BPRT without countdown timer, whereas a Weibull AFT duration model was established to model driver's BPRT with countdown timer. Three significant factors affecting the BPRT identified in both AFT models included yellow-onset distance from the stop line, yellow-onset approach speed, and deceleration rate. No matter whether the presence of countdown timer or not, BPRT increased as yellow-onset distance to the stop line or deceleration rate increased, but decreased as yellow-onset speed increased. The impairment of driver's BPRT due to countdown timer appeared to increase with yellow-onset distance to the stop line or deceleration rate, but decrease with yellow-onset speed. An increase in driver's BPRT because of countdown timer may induce risky driving behaviors (i.e., stop abruptly, or even violate traffic signal), revealing a weakness of countdown timer in traffic safety aspect.

Congestion pricing is an important component of urban intelligent transport system. The efficiency, equity and the environmental impacts associated with road pricing schemes are key issues that should be considered before such schemes are implemented. This paper focuses on the cordon-based pricing with distance tolls, where the tolls are determined by a nonlinear function of a vehicles’ travel distance within a cordon, termed as toll charge function. The optimal tolls can give rise to: 1) higher total social benefits, 2) better levels of equity, and 3) reduced environmental impacts (e.g., less emission). Firstly, a deterministic equilibrium (DUE) model with elastic demand is presented to evaluate any given toll charge function. The distance tolls are non-additive, thus a modified path-based gradient projection algorithm is developed to solve the DUE model. Then, to quantitatively measure the equity level of each toll charge function, the Gini coefficient is adopted to measure the equity level of the flows in the entire transport network based on equilibrium flows. The total emission level is used to reflect the impacts of distance tolls on the environment. With these two indexes/measurements for the efficiency, equity and environmental issues as well as the DUE model, a multi-objective bi-level programming model is then developed to determine optimal distance tolls. The multi-objective model is converted to a single level model using the goal programming. A genetic algorithm (GA) is adopted to determine solutions. Finally, a numerical example is presented to verify the methodology.

Weather is an important contributing factor for bus operations. It is essential to develop a weather-responsive bus scheduling system to improve the level of bus service. Bus travel time is...