Departure Interval Research Articles

Bus Scheduling of Overlapping Routes With Multi-Vehicle Types Based on Passenger OD Data

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.

Optimization of Urban Rail Transit Connection Scheme for Evacuating Large Volumes of Arriving Railway Passengers

Urban rail transit (URT) is an essential mode of travel used for evacuating passengers from railway stations. To provide timely and safe connecting services for passengers arriving at railway hubs, it is recommended for URT operators to develop a coordinated connection scheme considering the periodic incoming volume of railway passengers. According to the fluctuation characteristics of the large passenger flows in railways, this paper presents a reasonable scheme for increasing the capacity of URT trains. First, a method for calculating the effective evacuation capacity of URT trains and determining the time range that should be optimized according to the transportation capacity matching degree is proposed. The dwell time and departure interval are taken as decision variables in this adjustment period. Two constraints are considered: capacity convergence and the maximum safe capacity of the platform. Based on the consideration of reserved capacity for subsequent sections, the objective is to optimize the degree of matching between the effective evacuation capacity of the URT and the transfer demand of the arriving railway passengers. A multi-objective nonlinear integer-programming model is established for the coordinated connection of URT trains with large passenger flows, and a solution process of a train connection scheme is designed that involves a genetic algorithm. Finally, the effectiveness of the proposed model and algorithm is analyzed and verified by considering the Shanghai Hongqiao Hub-a transfer station between a high-speed railway and URT-as an example.

Energy Transfer Strategy for Urban Rail Transit Battery Energy Storage System to Reduce Peak Power of Traction Substation

In order to reduce the peak power of traction substation as much as possible and make better use of the configu-ration capacity of battery energy storage system (BESS) in urban rail transit, a BESS control strategy based on energy transfer is proposed. Based on the actual subway line data, the load characteristics of urban rail transit with different departure intervals are analyzed by using the simulation platform of urban rail transit traction power supply system. Then, based on the characteristics of urban rail traffic load and the high energy density of the battery, the SOC dynamic adjustment module and discharge threshold dynamic adjustment module were added. The discharge threshold is dynamically adjusted according to the SOC of the battery, and part of regenerative braking energy absorbed by the energy storage device is transferred from off-peak/flat peak period to peak period for release. The simulation model and 90 kW physical platform are used to verify the proposed control strategy, and the results show that the control strategy can effectively realize energy transfer. Not only can achieve energy saving and voltage stability, but also can effectively reduce the peak power of traction substation, bring more economic benefits.

Modeling Influencing Factors for Passenger Flow Growth of Modern Trams Using System Dynamics Method

Modern trams have been widely used around the world, especially in China. This paper explores the main influencing factors of modern trams’ passenger flow at the early operational stage. The system dynamics model is adopted for dealing with the problem on hand. Tram Line 1 in Huai’an, Jiangsu Province, China is selected as the case study. Data are collected using the RP and SP survey. The sensitivity test and extreme condition test are performed. The simulation results demonstrate that four variables (i.e., land development intensity, fares, service level, and transfer efficiency) significantly affect passenger flow. Land development intensity is the most significant factor, and the effect of service level on passenger flow is higher than that of the fares. The departure interval of 10 minutes is the maximum psychological limit that passengers can bear, and 2 RMB is a reasonable price. Such conclusions can provide guidance for the planning and design of modern trams and address the problem of shortage of passengers at an early stage.

Metro Passenger Flow Statistics Based on YOLOv3

Passenger flow real-time detection in per train compartment is the basis for realizing metro entrance flow restriction, platform waiting guidance and optimizing train departure interval. In this paper, we collect RGB images as the input of the improved YOLOv3 network, then we can get the statistics of passenger flow by detecting and tracking passengers’ heads. For the head detection part, this paper re-selects the number of anchors by using the k-means++ clustering method and removes the large-scale object detection module in the original YOLOv3 network. For the head tracking part, this paper proposes the probability gradient map, IOU-Max maximum matching algorithm and voting strategy, which make the stability of target tracking greatly enhanced. Compared with the original YOLOv3 network, our research results show that the accuracy of head detecting based on the improved YOLOv3 network increases from 90.1% to 97.84%, the recall rate increases from 85.4% to 94.7%, and the accuracy of the head tracking algorithm is as high as 98.87%, and the accuracy of passenger flow density detecting is as high as 95%. The measured detection speed reaches 47FPS on the TITAN X server, enabling real-time fast detection and tracking of targets.

Energy-Efficient Driving Strategies for Multi-Train by Optimization and Update Speed Profiles Considering Transmission Losses of Regenerative Energy

This paper aims at minimizing the total energy consumption of multi-train in an urban rail transit (URT) system by optimizing and updating speed profiles considering regenerative braking power losses on the catenary. To make full use of regenerative energy and decrease traction energy consumption simultaneously, energy-efficient control strategies of multi-train and a corresponding solution method are proposed. The running process of multi-train is divided into several sections based on passenger stations. Speed profiles of each train in each section are collaboratively optimized by searching only one transition point from the optimized single-train speed profile, which can be worked out by searching the switching point of coasting mode, and the optimized multi-train speed profiles are updated based on departure orders of trains. Moreover, an electrical network model is established to analyze energy flows, and dynamic losses of recovered regenerative energy on the line can be calculated. Besides, an improved optimization strategy of multi-train, which contains seven motion phases, is presented for steep slope. Simulation results based on Guangzhou Metro Line 8 verify the effectiveness of the proposed methods. Total energy consumption of optimized multi-train can be decreased by 6.95% compared with multi-train adopted single-train optimal control strategy, and the energy-saving rate of 21.08% can be achieved compared with the measured data by drivers under same trip time. In addition, the influence of departure interval on total energy consumption is analyzed and the optimal departure interval can be obtained.

Energy Consumption Optimization Model of Multi-Type Bus Operating Organization Based on Time-Space Network

As a new type of green bus, the pure electric bus has obvious advantages in energy consumption and emission reduction compared with the traditional fuel bus. However, the pure electric bus has a mileage range constraint and the amount of charging infrastructure cannot meet the demand, which makes the scheduling of the electric bus driving plans more complicated. Meanwhile, many routes are operated with mixing pure electric buses and traditional fuel buses. As mentioned above, we focus on the operating organization problem with the multi-type bus (pure electric buses and traditional fuel buses), aiming to provide guidance for future application of electric buses. We take minimizing the energy consumption of vehicles, the waiting and traveling time of passengers as the objectives, while considering the constraints of vehicle full load limitation, minimal departure interval, mileage range and charging time window. The energy consumption based multi-type bus operating organization model was formulated, along with the heuristic algorithm to solve it. Then, a case study in Beijing was performed. The results showed that, the optimal mixing ratio of electric bus and fuel bus vary according to the variation of passenger flow. In general, each fuel bus could be replaced by two pure electric buses. Moreover, in the transition process of energy structure in public transport, the vehicle scale keeps increasing. The parking yard capacity and the amount of charging facilities are supposed to be further expanded.

The Reliability Model of Artificial Monitoring on the Anomalous Event in Expressway Tunnels and Monte Carlo Simulation

Artificial monitoring remains to be a major way to detect anomalous events in expressway tunnels. To estimate the reliability of artificial monitoring on anomalous events in expressway tunnels, the video surveillance and mobile inspection based reliability models of artificial monitoring on the anomalous event in the expressway tunnel were built, and Monte Carlo method was applied to calculate the probability and mean time to detect the anomalous event at the specific time. The results showed that the Monte Carlo method could simulate video surveillance and mobile inspection, and obtain the probability distribution and mean time of detecting anomalous events. The mean time to spot the anomalous event was in reverse relation with the number of inspectors, the time of mobile inspection, and the reliability probability of the monitoring pre-warning system in tunnels and was in positive relationships with the departure interval. Combined with the actual operation cost, the model serves as a basis for the artificial monitoring package.

Setting Headways on a Bus Route under Uncertain Conditions

Urban public transport is a sustainable transportation strategy. Promoting public transport is an important means of urban transport sustainable development. Reasonable operation scheduling can increase the attractiveness of public transit systems and be conducive to the sustainability of transportation systems. Setting headways on a bus route is the key work of bus scheduling. For the refined management requirements of bus scheduling, this paper comprehensively considers the influence of three uncertainties on the bus route headway: passenger demand elasticity, which is an elasticity with respect to the supply of service, the randomness of the bus travel time between bus stops, and the abandoned passengers flow. A bus route headway allocation model is established. Considering the attractiveness of bus traveling and the interests of passengers, the objective functions are the maximum number of passengers waiting for the bus at each stop and the minimum number of passengers who fail to board. An enumeration combining a recursive algorithm under Monte Carlo random simulation conditions is designed to solve the problem. A comparison of the optimal bus departure interval (headway of the origin stop) under different conditions is studied using a numerical case. Under the same conditions, the results show that the model proposed in this paper can reduce the passengers waiting time and attract more passengers traveling by bus and it also meets the goal of sustainable public transport well. These findings are significant references for optimizing bus operations considering these uncertain factors.

Modeling of congestion propagation of rail transit passenger flow under delay conditions

In order to reduce the impact of train operation delay on passenger flow and operation of rail transit stations under emergency conditions, qualitative and quantitative methods are adopted to analyze the time-space effect of delay trains on rail transit lines and the crowding propagation process of the passenger flow, which provides a basis for train operation adjustment. Based on the timetable of trains and automatic fare collection swiping data of passengers when entering and leaving the station, the relationship between passengers and trains in time and space is analyzed and the time-space propagation model of delayed trains is established. This model comprehensively considers the relationship among the arriving passenger flow, the number of boarding and alighting passengers, the retention number of passengers and the passenger volume to reflect the impact of delayed trains on passengers' travel at each station. Besides, the train adjustment strategies are proposed. Case analysis shows that delay propagation has continuity in time and space. As the train operates on the line, the delays are propagated to follow-up stations, and the average delay time of trains can be effectively saved by shortening the departure interval and detaining trains.

Knotted-line: A Visual explorer for uncertainty in transportation system

Abstract Public transport system(PTS) is a complex system, and there are various uncertainties caused by traffic congestion, varying departure interval of buses, etc. In this paper, taking bus as an example, a novel visualization model knotted-line for transportation system uncertainties is proposed. The knotted-line visualization model consists of three parts, the violin plot, multi-layer ring and risk indicator. The violin plot shows the uncertainty of passengers’ waiting time. The multi-layer ring describes the uncertainty of bus arrival time. The risk indicator expresses the possibility of passenger spending time on the trip. A user study is conducted to evaluate the proposed visualization method. The results show that the proposed knotted-line visualization model is helpful for passengers to understand the uncertainty in transportation system and help users to make decisions.

Estimation of Time‐Varying Passenger Demand for High Speed Rail System

Passenger demand plays an important role in railway operation and organization, and this paper aims to estimate passenger time‐varying demand by simulating the ticket‐booking process for High Speed Rail (HSR) system. The ticket‐booking process of each OD pair can be partition into discrete booking phases by the times when the tickets of any itinerary had sold out. The ticket booking volume of each itinerary is reversely assigned to its corresponding expected departure intervals to obtain the time‐varying demand in each booking phase using the rooftop model, and the total time‐varying demand are estimated by summing the time‐varying demand distributions in all booking phases. Only with the data about the itinerary flow, the precedence relationship is introduced to constrain the ticket sold‐out order of all itineraries for each OD pair. Based on the precedence relationships of itineraries, two typical situations are proposed, in which the Single Booking Phase Reverse Assignment (SBPRA) algorithm and the Multiple Booking Phases Reverse Assignment (MBPRA) algorithm are proposed to estimate the time‐varying demand respectively. Case analysis on OD pair Beijing‐Shanghai are presented, and the validity analysis demonstrates that the error rates of SBPRA algorithm and MBPRA algorithm are 8.64% and 6.37%, respectively.

Chaotic Behavior of Traffic-Flow Evolution with Two Departure Intervals in Two-Link Transportation Network

In this study, the influence of traveler's departure time choice in day-to-day dynamic evolution of traffic flow in a transportation network is investigated. Combining historical information and real-time information, a dynamic evolution model of traffic flow with a study period divided into two intervals is proposed for a simple two-link network. Then, the evolution of network traffic flow is investigated using numerical experiments. Three types of information are considered: (1) only historical information, (2) only real-time information, and (3) both historical and real-time information. The results show that the dynamic evolution of network traffic flow under the three types of information is similar. However, the possibility of chaos occurrence under both historical and real-time information is smaller than that under two individual types of information. When chaos occurs, the chaotic behavior in traffic-flow evolution under only real-time information is relatively less complex than that under the other two types of information.

Optimal Pricing and Service for the Peak-Period Bus Commuting Inefficiency of Boarding Queuing Congestion

This paper proposes an equilibrium bus boarding model to investigate optimal pricing and service for peak-period bus commuting inefficiency of boarding queuing congestion. Commuters are assumed to choose their optimal time-of-use decision from home or the workplace to the bus. We found that: (1) when the earliest commuter boards the bus as soon as the bus arrives at the bus station, the dynamic boarding queuing congestion toll that eliminates the boarding queuing congestion creates social optimal equilibrium and the optimal bus departure interval during the peak period; (2) the optimal bus departure interval during the peak period is the time that the preceding bus riders spend on boarding, which means the relationship between service frequency and ridership does not conform to the square root principle: the optimal bus frequency is proportional to the square root of the number of commuters.

Intelligent scheduling method for energy saving operation of multi‐train based on genetic algorithm and regenerative kinetic energy

An intelligent scheduling method for energy saving operation of multi-train is proposed based on genetic algorithm and regenerative kinetic energy. Considering the morning and evening peak, departure time, and total vehicle number, the energy consumption optimisation model of multidimensional state vector subspace of train is established for the train departure interval with the lowest total energy consumption. Two intelligent control models are used to formulate the time interval scheduling plan depending on whether to consider the morning and evening peak. The train operation diagram is solved by Matlab, and the validity and feasibility of the proposed algorithm are verified. This study provides an intelligent and efficient algorithm and solution for multi-train intelligent scheduling problem.

The Modeling and Simulation of DC Traction Power Supply Network for Urban Rail Transit Based on Simulink

In order to study the influence of the state of train on the power supply network of urban rail transit, it is necessary to model and analyse the DC traction power supply system and vehicles. Based on the analysis of the structure of power supply system for urban rail transit and according to the operating principle of train, the modeling method of traction power supply system is proposed. The model of DC traction power supply system and train are established in the Simulink environment. The speed, position and power of the train vary with time through simulation, so the electrical value of train operation such as voltage that changes over time can be obtained under the model. The simulation results and the measured data are consistent in the trend of change, but there is a certain difference in corresponding values (fluctuated in an acceptable range), which verifies the validity of the model. In addition, it compares the energy consumption of substations at different departure intervals.

Optimising departure intervals for multiple bus lines with a multi‐objective model

A multi-objective model was developed to optimize departure intervals synchronously for multiple bus lines. Then, a Genetic Algorithm with “Elitist Preservation” strategy combining the economical method of “dynamic scoring” (GA-EPDS) was proposed to solve the multi-objective model. The proposed method included three objectives: the first objective was to maximize the bus operation profits; the second objective was to minimize the passengers’ transfer waiting time; and the last one was to minimize passengers’ costs. Transfer waiting time was crucial for multiple bus lines and long transfer waiting time would decrease the satisfaction of passengers, so transfer waiting time was regarded as a single objective. In addition, an evaluation function, which was obtained through a “dynamic scoring” method, was formulated to estimate whether the three objective functions reached a global optimum. In order to improve the solution generated in terms of computational effort and convergence, a GA-EPDS was designed to solve the multi-objective model. Finally, the proposed approach was applied in a case study of an actual network. The numerical results based on different scale instances and different traffic conditions demonstrate that our proposed model and method are effective and feasible to optimize departure intervals for multiple bus lines.

A synergistic energy-efficient planning approach for urban rail transit operations

Large-scale development of urban rail transit has attracted attention owing to its sizable energy consumption. Energy-efficient planning can enhance the distribution and utilization of limited energy resources. This study proposes a two-stage urban rail transit operation planning approach comprising running time allocation and regenerative energy utilization to save energy consumption. The proposed models and algorithms holistically deal with inter-station running time synergy which utilizes surplus running time to achieve minimum energy consumption. They also implement the hauling and braking synergy of multiple trains in multiple trips, with adjustments to departure intervals and dwelling times, to maximize the regenerative energy real-time utilization rate. The algorithms utilize chromosomes in genetic algorithm to represent possible operation stage combinations, conduct feasible direction iterations to facilitate surplus-time effective allocations, and maximize the derived overlap time for operation synergy of trains under the precondition of energy-efficient train movements between stations. A case study of the metro line demonstrates that considerable energy saving is achievable through the proposed planning approach.

Research on Energy-Saving Operation Strategy for Multiple Trains on the Urban Subway Line

Energy consumption for multiple trains on the urban subway line is predominantly affected by the operation strategy. This paper proposed an energy-saving operation strategy for multiple trains, which is suitable for various line conditions and complex train schedules. The model and operation modes of the strategy are illustrated in detail, aiming to take full use of regenerative braking energy in complex multi-train systems with different departure intervals and dwell times. The computing method is proposed by means of the heuristic algorithm to obtain the optimum operation curve for each train. The simulation result based on a real urban subway line is provided to verify the correctness and effectiveness of the proposed energy-saving operation strategy.

Analysis of the equilibrium trip cost accounting for the fuel cost in a single-lane traffic system without late arrival

In this paper, we introduce the fuel cost into each commuter’s trip cost, define a new trip cost without late arrival and its corresponding equilibrium state, and use a car-following model to explore the impacts of the fuel cost on each commuter’s departure time, departure interval, arrival time, arrival interval, traveling time, early arrival time and trip cost at the above equilibrium state. The numerical results show that considering the fuel cost in each commuter’s trip cost has positive impacts on his trip cost and fuel cost, and the traffic situation in the system without late arrival, i.e., each commuter should explicitly consider the fuel cost in his trip cost.