Impact of Carriage Crowding Level on Bus Dwell Time: Modelling and Analysis

This paper develops two types of estimation models to quantify the impacts of carriage crowding level on bus dwell time. The first model (model I) takes the crowding level and the number of alighting and boarding passengers into consideration and estimates the alighting time and boarding time, respectively. The second model (model II) adopts almost the same regression method, except that the impact of crowding on dwell time is neglected. The analysis was conducted along two major bus routes in Harbin, China, by collecting 640 groups of dwell times under crowded condition manually. Compared with model II, the mean absolute error (MAE) of model I is reduced by 137.51%, which indicates that the accuracy of bus dwell time estimation could be highly improved by introducing carriage crowding level into the model. Meanwhile, the MAE of model I is about 3.9 seconds, which is acceptable in travel time estimation and bus schedule

Trip energy consumption estimation for electric buses

This study aims to develop a trip energy consumption (TEC) estimation model for the electric bus (EB) fleet planning, operation, and life-cycle assessment. Leveraging the vast variations of temperature in Jilin Province, China, real-world data of 31 ​EBs operating in 14 months were collected with temperatures fluctuating from −27.0 ​to 35.0 ​\ub0C. TEC of an EB was divided into two parts, which are the energy required by the traction and battery thermal management system, and the energy required by the air conditioner (AC) system operation, respectively. The former was regressed by a logarithmic linear model with ambient temperature, curb weight, travel distance, and trip travel time as contributing factors. The optimum working temperature and regression parameters were obtained by combining Fibonacci and Weighted Least Square. The latter was estimated by the operation time of the AC system in cooling mode or heating mode. Model evaluation and sensitivity analysis were conducted. The results show that: (i) the mean absolute percentage error (MAPE) of the proposed model is 12.108%; (ii) the estimation accuracy of the model has a probability of 99.7814% meeting the requirements of EB fleet scheduling; (iii) the MAPE has a 1.746% reduction if considering passengers’ boarding and alighting

Optimal charging plan for electric bus considering time-of-day electricity tariff

Purpose: The purpose of this study is to develop an optimization method for charging plans with the implementation of time-of-day (TOD) electricity tariff, to reduce electricity bill. Design/methodology/approach: Two optimization models for charging plans respectively with fixed and stochastic trip travel times are developed, to minimize the electricity costs of daily operation of an electric bus. The charging time is taken as the optimization variable. The TOD electricity tariff is considered, and the energy consumption model is developed based on real operation data. An optimal charging plan provides charging times at bus idle times in operation hours during the whole day (charging time is 0 if the bus is not get charged at idle time) which ensure the regular operation of every trip served by this bus. Findings: The electricity costs of the bus route can be reduced by applying the optimal charging plans. Originality/value: This paper produces a viable option for transit agencies to reduce their operation costs

Optimization of electric bus scheduling considering stochastic volatilities in trip travel time and energy consumption

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

Modeling Unidirectional Pedestrian Movement: An Investigation of Diffusion Behavior in the Built Environment

Unidirectional pedestrian movement is a special phenomenon in the evacuation process of large public buildings and urban environments at pedestrian scale. Several macroscopic models for collective behaviors have been built to predict pedestrian flow. However, current models do not explain the diffusion behavior in pedestrian crowd movement, which can be important in representing spatial-temporal crowd density differentiation in the movement process. This study builds a macroscopic model for describing crowd diffusion behavior and evaluating unidirectional pedestrian flow. The proposed model employs discretization of time and walking speed in geometric distribution to calculate downstream pedestrian crowd flow and analyze movement process based on upstream number of pedestrians and average walking speed. The simulated results are calibrated with video observation data in a baseball stadium to verify the model precision. Statistical results have verified that the proposed pedestrian diffusion model could accurately describe pedestrian macromovement behavior within the margin of error

Optimization of signal-timing parameters for the intersection with hook turns

A Hook Turn (HT) traffic control scheme has been successfully implemented in urban Melbourne (Australia) ever since 1950s, for the regulation of right-turning vehicles at the intersections (in traffic system where driving is on the left). This paper addresses the optimal signal-timing of the HT scheme, which is still an open question in the literature. Under the HT scheme, right-turning vehicles should enter the intersection and stop at a waiting area. Hence, it is common to have a spillback from these vehicles if the right-turning volume is high. This paper provides an in-depth analysis of the spillback phenomenon on the traffic movements and the average delays, and proposes the models for the calculation of average delay in different cases. With the aim of minimizing the average delay of all the vehicles, a nonlinear integer-programming model is proposed for the optimal signal-timing problem of HT scheme. A Genetic Algorithm (GA) is used to solve this model, considering the complexity of its objective function. A realistic example developed based on one intersection with HT in urban Melbourne is adopted to assess the proposed methodology. Based on real survey data in morning peak and nonpeak hours, we compare the existing signal plan and optimal plan. The numerical test shows that compared with the existing plan, the optimal plan can reduce the average delay for 12.05% in peak hour and 19.96% in nonpeak hour. Sensitive analysis is also conducted to investigate the variation of right-turning ratio on the intersection operational performance

Improving schedule adherence based on dynamic signal control and speed guidance in connected bus system

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

An Adaptive Model for Calculating the Correlation Degree of Multiple Adjacent Signalized Intersections

As an important component of the urban adaptive traffic control system, subarea partition algorithm divides the road network into some small subareas and then determines the optimal signal control mode for each signalized intersection. Correlation model is the core of subarea partition algorithm because it can quantify the correlation degree of adjacent signalized intersections and decides whether these intersections can be grouped into one subarea. In most cases, there are more than two intersections in one subarea. However, current researches only focus on the correlation model for two adjacent intersections. The objective of this study is to develop a model which can calculate the correlation degree of multiple intersections adaptively. The cycle lengths, link lengths, number of intersections, and path flow between upstream and downstream coordinated phases were selected as the contributing factors of the correlation model. Their jointly impacts on the performance of the coordinated control mode relative to the isolated control mode were further studied using numerical experiments. The paper then proposed a correlation index (CI) as an alternative to relative performance. The relationship between CI and the four contributing factors was established in order to predict the correlation, which determined whether adjacent intersections could be partitioned into one subarea. A value of 0 was set as the threshold of CI. If CI was larger than 0, multiple intersections could be partitioned into one subarea; otherwise, they should be separated. Finally, case studies were conducted in a real-life signalized network to evaluate the performance of the model. The results show that the CI simulates the relative performance well and could be a reliable index for subarea partition

Automatic Estimation Method for Intersection Saturation Flow Rate Based on Video Detector Data

Saturation flow rate (SFR) is a fundamental parameter to the level of service evaluation, lane capacity calculation, and signal timing plan optimization at signalized intersections. It is affected by a variety of factors including weather conditions, lane width, and the type of the driver. How to accurately estimate the SFR remains one of the most important tasks in traffic engineering. Existing studies generally rely on the field measurement method which requires a large number of people collecting data at the intersection. As a result, the method incurs a high economic cost and cannot adapt to the dynamic change of SFR. In recent years, video detectors have been widely installed at intersections which are capable of recording the time each vehicle passes the stop line, the number plate of each vehicle, and the vehicle type. This paper therefore aims to propose an automatic estimation method for the SFR based on video detector data in order to overcome the limitation of the field measurement method. A prerequisite for estimating the SFR is to recognize the saturation headway. We consider the actual vehicle headway as time series and build an auxiliary regression equation whose parameters are estimated through the ordinary least squares method. We employ the Dickey-Fuller test to verify whether the headways in the time series are saturation headways. An iterative method using quantiles is proposed to filter out abnormal data. The SFR is finally calculated using the average value of saturation headways. To demonstrate the proposed method, we conduct a case study using data from an intersection with three entrance lanes in Qujing city, Yunnan Province, China. The overall estimation process is displayed and the impacts of quantile selection and data duration on the estimation accuracy are analyzed

Optimal Electric Bus Scheduling Based on the Combination of All-Stop and Short-Turning Strategies

The emission of greenhouse gases from public transportation has aroused extensive public attention in recent years. Electric buses have the advantage of zero emission, which could prevent the further deterioration of environmental problems. Since 2018, the number of electric buses has exceeded that of traditional buses. Thus, it is an inevitable trend for the sustainable development of the automobile industry to replace traditional fuel buses, and developing electric buses is an important measure to relieve traffic congestion. Furthermore, the bus scheduling has a significant impact on passenger travel times and operating costs. It is common that passenger demand at different stops is uneven in a public transportation system. Since applying all-stop scheduling only cannot match the passenger demand of some stops with bus resources, this paper proposes an integrated all-stop and short-turning service for electric buses, reducing the influence of uneven ridership on load factor to enhance transit attractiveness. Simultaneously, considering the time-of-use pricing strategy used by the power sector, the combinational charging strategy of daytime and overnight is proposed to reduce electricity costs. Finally, the branch-and-price algorithm is adopted to solve this problem. Compared with all-stop scheduling, the results demonstrate a reduction of 13.5% in total time cost under the combinational scheduling