Identifying Critical Components of a Public Transit System for Outbreak Control

Modern public transport networks provide an efficient medium for the spread of infectious diseases within a region. The ability to identify components of the public transit system most likely to be carrying infected individuals during an outbreak is critical for public health authorities to be able to plan for outbreaks, and control their spread. In this study we propose a novel network structure, denoted as the vehicle trip network, to capture the dynamic public transit ridership patterns in a compact form, and illustrate how it can be used for efficient detection of the high risk network components. We evaluate a range of network-based statistics for the vehicle trip network, and validate their ability to identify the routes and individual vehicles most likely to spread infection using simulated epidemic scenarios. A variety of outbreak scenarios are simulated, which vary by their set of initially infected individuals and disease parameters. Results from a case study using the public transit network from Twin Cities, MN are presented. The results indicate that the set of transit vehicle trips at highest risk of infection can be efficiently identified, and are relatively robust to the initial conditions of the outbreak. Furthermore, the methods are illustrated to be robust to two types of data uncertainty, those being passenger infection levels and travel patterns of the passengers.     

实时信息下的乘客路径选择行为

智能公交系统伴随着智能交通信息系统的发展而逐渐普及,其目的是向乘客提供各种实时交通信息,以提高出行的便利性和灵活性,最终实现公交出行分担率的提升。针对公交网络的特殊性,提出符合乘客路径选择行为且易于确定的广义路径定义,以成都公交电子站牌信息为背景,设计问卷对乘客路径选择行为及出行意向进行调查。采用定性和定量分析相结合的分析方法,基于随机效用理论,建立包括路径选择方案特性变量和乘客个人社会经济属性特性变量为解释变量的Logit和混合Logit路径选择模型,运用蒙特卡洛模拟和极大似然法完成参数估计。分析结果表明,混合Logit模型能更合理地解释由个体偏好而导致的路径选择行为差异,有助于对复杂公交行为的理解,以便更好地用以指导实践     

Passenger flow prediction in bus transportation system using deep learning

The forecasting of bus passenger flow is important to the bus transit system’s operation. Because of the complicated structure of the bus operation system, it’s difficult to explain how passengers travel along different routes. Due to the huge number of passengers at the bus stop, bus delays, and irregularity, people are experiencing difficulties of using buses nowadays. It is important to determine the passenger flow in each station, and the transportation department may utilize this information to schedule buses for each region. In Our proposed system we are using an approach called the deep learning method with long short-term memory, recurrent neural network, and greedy layer-wise algorithm are used to predict the Karnataka State Road Transport Corporation (KSRTC) passenger flow. In the dataset, some of the parameters are considered for prediction are bus id, bus type, source, destination, passenger count, slot number, and revenue These parameters are processed in a greedy layer-wise algorithm to make it has cluster data into regions after cluster data move to the long short-term memory model to remove redundant data in the obtained data and recurrent neural network it gives the prediction result based on the iteration factors of the data. These algorithms are more accurate in predicting bus passengers. This technique handles the problem of passenger flow forecasting in Karnataka State Road Transport Corporation Bus Rapid Transit (KSRTCBRT) transportation, and the framework provides resource planning and revenue estimation predictions for the KSRTCBRT.

     

基于不确定需求的公共交通网络鲁棒性优化方法

为了提高城市不同类型公共交通所组成的线网的鲁棒性,从公共交通线路建设成本、乘客出行的总时间以及乘客总换乘次数等方面确定公共交通网络的服务性能模型,在此基础上通过计算方案目标值与期望值的差值来确定公交网络的鲁棒性;由于存在随机不确定需求,在传统免疫克隆算法基础上对变异操作进行改进用于对优化模型求解。结合算例分析发现,线路建设成本、乘客总出行时间以及乘客总换乘次数的参数值对于优化结果具有显著影响;另外鲁棒性参数取值也会对计算结果产生一定影响,通过算例验证了优化方法的可行性。     

The Optimal Transit Fare Structure under Different Market Regimes with Uncertainty in the Network

This paper proposes a network-based model for investigating the optimal transit fare structure under monopoly and oligopoly market regimes with uncertainty in the network. The proposed model treats the interaction between transit operators and transit passengers in the market as a two-level hierarchical problem with the transit operator sub-model at the upper-level and the transit passenger sub-model at the lower-level. The upper-level problem is to determine the fare structure so as to optimize the objective function of the transit operators, whereas the lower-level problem represents the path choice equilibrium of the transit passengers. In order to consider the uncertainty effects on transit network, the proposed model incorporates the unreliability component of transit services into the passenger disutility function, which is mainly due to variations of the in-vehicle travel time and the dwelling time of transit vehicles at stops. With the use of the proposed model, a numerical example is given to assess the impacts of the market regimes and the unreliability of the transit services on the optimal transit fare structure.     

基于Anylogic的轨道交通换乘能力改进的研究

研究轨道交通换乘枢纽与市内其它交通方式的衔接与协调,是城市公共交通线网优化的主要内容之一。它能减少出行过程中的等待时间,缩短人们出行时间,提高公交服务质量,并保证客运交通的高效率,也能更好地促进城市轨道交通与其它交通方式的协调发展。以武汉地铁某换乘站研究对象,通过实地采集其基本设施的位置和数量、入站行人和出站行人的数量,并利用Anylogic仿真软件,建立站点的平面布置图空间模型和行人流程图。首先通过仿真软件仿真找出换乘客流瓶颈部位,其次分别对其提出改善建议并加以修改仿真模型。最终仿真结果表明,改善建议使得换乘站的换乘效率都得到提高。     

Individuals among commuters: Building personalised transport information services from fare collection systems

This work investigates how data from public transport fare collection systems can be used to analyse travellers’ behaviour, and transform travel information systems that urban residents use to navigate their city into personalised and dynamic systems that cater for each passenger’s unique needs. In particular, we show how fare collection data can be used to identify behavioural differences between passengers: we thus advocate for a personalised approach to delivering transport related information to travellers. To demonstrate the potential for personalisation we compute trip time estimates that more accurately reflect the travel habits of each passenger. We propose a number of algorithms for personalised trip time estimations, and empirically demonstrate that these approaches outperform both a non-personalised baseline computed from the data, as well as published travel times as currently offered by the transport authority. Furthermore, we show how to easily scale the system by pre-clustering travellers. We close by outlining the wide variety of applications and services that may be fuelled by fare collection data.     

Towards proximity-based passenger sensing on public transport buses

While substantial research on intelligent transportation systems has focused on the development of novel wireless communication technologies and protocols, relatively little work has sought to fully exploit proximity-based wireless technologies that passengers actually carry with them today. This paper presents the real-world deployment of a system that exploits public transit bus passengers’ Bluetooth-capable devices to capture and reconstruct micro- and macro-passenger behavior. We present supporting evidence that approximately 12 % of passengers already carry Bluetooth-enabled devices and that the data collected on these passengers captures with almost 80 % accuracy the daily fluctuation of actual passengers flows. The paper makes three contributions in terms of understanding passenger behavior: We verify that the length of passenger trips is exponentially bounded, the frequency of passenger trips follows a power law distribution, and the microstructure of the network of passenger movements is polycentric.     

Transit network design by Bee Colony Optimization

The transit network design problem is one of the most significant problems faced by transit operators and city authorities in the world. This transportation planning problem belongs to the class of difficult combinatorial optimization problem, whose optimal solution is difficult to discover. The paper develops a Swarm Intelligence (SI) based model for the transit network design problem. When designing the transit network, we try to maximize the number of satisfied passengers, to minimize the total number of transfers, and to minimize the total travel time of all served passengers. Our approach to the transit network design problem is based on the Bee Colony Optimization (BCO) metaheuristics. The BCO algorithm is a stochastic, random-search technique that belongs to the class of population-based algorithms. This technique uses a similarity among the way in which bees in nature look for food, and the way in which optimization algorithms search for an optimum of a combinatorial optimization problem. The numerical experiments are performed on known benchmark problems. We clearly show that our approach, based on the BCO algorithm, is competitive with other approaches in the literature, and it can generate high-quality solutions.     

基于公共交通的高精度时空可达模型与算法

公共交通作为我国城市居民的主要出行方式,对其可达性的研究具有非常重要的价值和意义.然而,由于站点位置、固定线路、时刻表等条件的限制,使得公共交通可达性的研究具有一定的特殊性.针对已有研究存在的可达精度不高或者无法进行大规模可达分析等问题,基于路网、公交网络、地铁网络和时刻表信息建立高精度时空网络模型,设计时间依赖条件下枢纽站点和A^*算法相结合的快速公交换乘算法.以武汉市为例,对其进行大规模高精度时空可达分析,证明了模型的可靠性和算法的高效性.     

民航旅客行程状态推断方法研究

现有的民航旅客行程状态推断的相关方法不能对旅客历史行为序列中远距离项的依赖关系建模,且忽略了行程子结构,为此提出一种新的模型来推断旅客行程状态。首先通过图神经网络挖掘出行序列任意机场间的转移模式;其次,构造层次注意力依次在机场级别和行程级别捕获旅客的短期和长期出行偏好;最后融合旅客的短期和长期出行偏好进行分类。实验结果表明,图神经网络的消息传递机制突破了距离的限制,有效捕获了旅客出行序列中任意机场间复杂的关系,模型在多项性能指标上效果很好,构建的图神经网络和注意力机制结合的方法可获得更好的性能;另外,结合实际的应用场景,融入了额外的特征进行信息补充取得了更好的推断效果。     

An observational comparison of the older and younger bus passenger experience in a developing world city

This study was an unobtrusive observational analysis of 333 older and younger bus passengers in Guadalajara, Mexico. A set of data were collected for each observed passenger, as well as more general observations related to driver behaviour, bus design and bus service characteristics. There were significant differences between older and younger passengers in terms of boarding and alighting times, use of handrails, seat location preferences, passenger stability and coping strategies in order to maintain postural stability. The conditions of travel are conducive to a poor passenger experience for the older passengers in particular. Although the problems may be attributed to bus design and driver behaviour typical of that in developing countries, they are also influenced by the wider transport infrastructure, and a lack of a regulatory regime which places drivers under time pressure and in direct competition with each other.

Practitioner Summary: Bus services must cater for all ages of passengers, including the elderly. This unobtrusive observational study investigated the passenger experience in a developing world city. Bus and wider service design were found to compromise the journey experience, with the older users being particularly negatively impacted. Design recommendations are provided.     

A multi-criteria route planning model based on fuzzy preference degrees of stops

Integrated utilization of new technologies such as smart phones, tablet devices, and satellite maps has entered our daily lives recently. Nevertheless, many new applications are being developed mostly based on these technologies. The optimal route planning, which makes use of the public transport network structure between any selected origin and destination points, is one of the interesting applications among them. Route planning applications used today mostly focus on the aspects such that passengers use nearest stops around origin and destination geographical points, or use set of stops around these points within some walking radius. In these applications, which work on the classical (crisp) logic base, all stops on the walking distance have the same preference degree. However, in this study a novel fuzzy model is proposed which also takes into account preferences such as the stop’s activity, and count of transit lines passing through the stop besides the walking distance. Using all these three preferences, aggregated fuzzy preference degrees of stops are calculated. The “optimum” routes between any origin and destination pair are constructed using feasible transfer points, which are chosen among the alternatives having the highest preference degrees overall. Fuzzy neighborhood relations such as “stop-stop”, “stop-line”, and “line-line” are introduced in order to employ in preference degree evaluations.Apart from the aggregated degree of the preferences mentioned above, we also consider to minimize the total number of transit stops travelled on any route for establishing optimal routes. This additional preference can be described the time duration spent on transport vehicles, such as buses, trains, subways or ferries. Therefore, we propose a two-criteria route-planning problem in this study, where we try to maximize the aggregated preference degree of a route and to minimize the number of stops used on a route. Fuzzy optimal solutions for this problem are constructed via γ-level solutions of the fuzzy problem and a heuristic algorithm providing these solutions is proposed. This model and its algorithm can be considered as an optimal route search engine for mobile applications that could be used by urban public transport passengers.     

A fast hybrid computer vision technique for real-time embedded bus passenger flow calculation through camera

Bus passenger flow calculation system is a critical part of the smart public transportation framework. Bus passenger flow information can help to make data statistics report of the passenger at a bus station which can be used by public transport operator to evaluate the quality of the transportation. Statistics report of crowded passengers in the bus station help managers to understand the bus transit operations, can provide the database for the intelligent transportation scheduling, help to provide more and better services for passengers, overall data statistics of passengers has important practical significance to improve public transport environment. This paper presents a passenger counting algorithm based on hybrid machine learning approach. In the first step, an advanced method is used to extract the Histogram of oriented gradients (HOG) feature of passenger’s heads. Classification of head features is done by using support vector machine (SVM) as a classifier for the liner model. Heads are detected successfully after performing all steps. In next step Kanade-Lucas-Tomasi (KLT) is used to reality head tracking, the multiple target tracking is achieved and the head motion trajectory of passenger target is captured stably. At last, the trajectory is analyzed and the automatic counting of bus passenger flow is realized. In the last step, the proposed algorithm is move to embedded system for practical implementation. In this paper, the algorithm intends to use ADSP-BF609 embedded platform for transplantation. The experimental results demonstrate that the statistical accuracy of the proposed algorithm is enhanced successfully; especially during the daytime with the good illustration, the effective counting of the passenger flow is achieved and the inward and outward passenger counting can be realized. In this paper three feature extraction models are used namely local binary patterns, histograms of oriented gradients and binarized statistical image in order to get accurate features. Furthermore, three common classification techniques including naïve bayes classifier, boosted tress and support vector machines are used for fine classification of extracted vectors obtained from different features extractors model. 94.50% accuracy is achieved when support vector machine (SVM) classifies the features extracted using Histogram of oriented gradients (HOG). SVM surpasses the accuracy obtained by Boosted tree namely 81.30% using Histogram of oriented gradients (HOG) features.

     

Time-optimal and privacy preserving route planning for carpool policy

To alleviate the traffic congestion caused by the sharp increase in the number of private cars and save commuting costs, taxi carpooling service has become the choice of many people. Current research on taxi carpooling services has focused on shortening the detour distances. While with the development of intelligent cities, efficiently match passengers and vehicles and planning routes become urgent. And the privacy between passengers in the taxi carpooling service also needs to be considered. In this paper, we propose a time-optimal and privacy-preserving carpool route planning system via deep reinforcement learning. This system uses the traffic information around the carpooling vehicle to optimize passengers’ travel time, not only to efficiently match passengers and vehicles but also to generate detailed route planning for carpooling vehicles. We conducted experiments on an Internet of Vehicles simulator CARLA, and the results demonstrate that our method is better than other advanced methods and has better performance in complex environments.

     

MVB总线的轨道交通车辆乘客计数系统设计

为了对城市轨道交通各线路的客流人数进行统计,设计了一种基于 MVB总线的城市轨道交通车辆乘客计数系统,介绍了该系统的框架,阐述了该系统的软硬件结构及其功能.构建了一套模拟测试环境,模拟了城市轨道交通车辆车载网络及客流情况,对该系统进行功能及性能测试.测试结果表明,该系统 MVB通信功能稳定可靠,计数准确度较高,能基本满足城市轨道交通车辆乘客计数的准确度要求.     

基于人工蜂群算法的轨道交通列车行车间隔优化

轨道交通运营组织作为轨道交通运营企业管理的核心,在降低企业运营成本、提升服务水平和旅客出行效率方面起着非常重要的作用。提出一种基于人工蜂群（ABC）优化算法的列车行车间隔优化策略,在考虑运营企业和旅客各自利益的基础上,以列车发车间隔为决策变量,建立旅客平均候车时间最小和列车等候时间最大的双目标非线性规划模型。采用ABC算法对模型进行优化求解,结合京津城际铁路某日不同时段客流基础数据进行仿真,实例验证了所提算法和模型的有效性。     

基于增强学习的网格化出租车调度方法

高度信息化的网格化城市管理可以为出租车运营优化提供新的实时动态乘客需求信息和车辆位置信息。以此为契机,针对城市出租车空驶率高和司乘匹配率低的问题,提出了一种网格化的出租车实时动态调度的增强学习控制方法。通过为出租车提供空驶巡游的动态最佳路线,新的控制方法旨在提高出租车的服务效率,并降低乘客的等待时间。首先,以城市单元网格为基础,明确出租车调度的关键问题;其次,以空驶路线的动态调整为控制手段,建立调度的增强学习模型;最后,给出求解模型的Q学习算法,并通过算例验证新调度方法的有效性。研究表明新方法可以有效提高司乘匹配率、增加总的出租车运营收入、减少乘客平均等车时间和减少总的出租车空驶时间。     

Online-review analysis based large-scale group decision-making for determining passenger demands and evaluating passenger satisfaction: Case study of high-speed rail system in China

High-speed rail (HSR) has become an essential mode of public transportation in China and is likely to remain so for the foreseeable future. To promote the development of the HSR industry, a high level of passenger satisfaction must be ensured, which means that passenger satisfaction must be assured. Focusing on HSR in-cabin factors that affect the travel experience of HSR passengers, this study aims to determine passenger demands (PDs) and to evaluate passenger satisfaction by using a combination of online review analysis and large-scale group decision-making (LSGDM). By using web crawler technology, online reviews related to HSR were harvested from a microblogging platform to extract PD data and information. The six PDs that reflect the most frequent concerns of passengers were identified by analyzing the online reviews. The level of satisfaction of passengers with respect to these PDs was analyzed based on the online responses from 100 HSR passengers and by adopting the interval-valued two-tuple linguistic representation model. The final degrees of satisfaction and rankings of the PDs were then determined by using the LSGDM approach with the k-means clustering method and a consensus-reaching process. This research thus constructs an index system of HSR passenger satisfaction evaluation based on online-review analysis and evaluates the process by using LSGDM approaches. The conclusions provide insights into the improvements desired by HSR passengers for in-cabin services and to improve passenger satisfaction.     

基于神经网络的城市轨道交通短期客流预测研究

该文结合神经网络来研究城市轨道交通中短期客流预测问题。设计出了基于自回归神经网络的轨道交通客流预测模型、模型描述及其模型训练算法。通过matlab仿真实验来验证预测模型的性能,优于将最小二乘支持向量机与离散一维Daub4小波分析结合起来预测效果。