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.     

一种基于车流量的司乘推荐模型

作为城市交通的基础设施,出租车在日常交通中起着重要作用。随着城市规模的扩大,人们的出行需求不断增加,然而出租车的分布与叫车需求分布之间难以匹配,从而导致打车困难问题的出现,这一现象在大型城市尤其明显。造成打车难的主要原因在于司乘之间信息不能互通,GPS、车联网等技术能够提供车辆位置、运行轨迹等信息,通过对这些信息数据进行处理,可以获得有价值的信息,将其提供至司乘双方能够提升出租车运营效率。现有信息处理方法较为简单,忽略了较多关键影响因素,难以达到理想的效果。因此,本文提出了一种基于出租车轨迹和路网数据来衡量打车难度的出租车流量模型,并通过综合时间、天气等因素对模型进行优化,提升了模型的实用性。基于该模型利用数据挖掘算法抽取有用信息,提供给出租车司机和乘客。最后,本文基于实际出租车数据对模型进行实验验证,结果证明了模型的有效性及实用性。     

Dynamic Passenger Assignment for Major Railway Disruptions Considering Information Interventions

Passenger assignment models for major disruptions that require trains to be cancelled/short-turned in railway systems are rarely considered in literature, although these models could make a significant contribution to passenger-oriented disruption timetable design/rescheduling. This paper proposes a dynamic passenger assignment model, where the passengers who start travelling before, during and after the disruption are all considered. The model ensures that on-board passengers are given priority over waiting passengers, and waiting passengers are boarding under the first-come-first-serve rule. Moreover, the model allows information interventions by publishing information about service variations and train congestion at different locations with the aim of distributing passengers wisely to achieve less travel time increase due to the disruption. Discrete event simulation is adopted to implement the model, where loading/unloading procedures are realized and passengers re-plan their paths based on the information they receive. The model tracks individual travels, which helps to evaluate a disruption timetable in a passenger-oriented way.

     

Navigation in desktop virtual environments: an evaluation and recommendations for supporting usability

Virtual reality (VR) can provide useful tools for a variety of applications. However, for these tools to be effective, they must be easy to use. In virtual environments (VEs), usability is impaired by poorly designed navigation systems. Insufficient realism and missing physiological orientation and motion cues impair spatial learning in desktop VEs. Capabilities for navigation in a VE are far more varied than in reality; so much greater flexibility can be offered, but designing VEs with too many options can overwhelm users. To assist designers in building effective, usable navigation systems for VEs, navigation techniques must be evaluated to identify which features actually support users in accomplishing their tasks and which features create unnecessary problems.This study evaluates navigation in two different VEs to develop recommendations for the design of navigation systems in desktop VEs. The study consists of an objective assessment of navigation control dynamics, a guideline-based evaluation and a review of data collected during two experimental studies. The findings indicate that real-world constraints, specialised navigation techniques and feedback regarding location and direction of travel are needed to support navigation in desktop VEs.     

Solution of the Dial-a-Ride Problem with multi-dimensional capacity constraints

The Dial‐a‐Ride Problem (DARP) consists of planning routes and schedules for picking up and delivering users within user‐specified time windows. Vehicles of a given fleet with limited capacity depart from and end at a common depot. The travel time of passengers cannot exceed a given multiple of the minimum ride time. Other constraints include vehicle capacity and vehicle route duration. In practice, scheduling is made more complicated by special user requirements and an inhomogeneous vehicle fleet. The transportation of elderly and handicapped people is an important example, as space for wheelchairs is limited and a lift is required. In this study, we present a modified insertion heuristic to solve the DARP with multi‐dimensional capacity constraints, and the performance of the proposed algorithm is tested in simulation. We show that the proposed methodology is effective when compared with the classic algorithms.     

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     

My choice,your problem? Mandating IT use in large organisational networks

On the basis of a four‐year exploratory study of a mandatory information systems implementation by an Italian, multibillion‐dollar dairy cooperative with 2200 members, this paper describes how key stakeholders engage in dynamic transformation processes that shape the technology, the users' practices and the organisation itself. In doing so, this study responds to calls for process‐oriented longitudinal explorations and suggests an alternative path of adoption in which the technology becomes the reification of a bi‐directional discourse about the transformation of practices for the entire network of organisations. In presenting this alternative path, this study unveils a five‐phase change process that both altered perceptions of the technology and its possibilities at the same time resolving tensions among the drivers and users of the mandated system. © 2016 John Wiley & Sons Ltd     

Passenger travel behaviour on Chinese high‐speed railways using machine learning based on revealed‐preference data

Existing studies on the selection of railway travel modes almost always compare high‐speed trains and regular‐speed trains. In this study, three high‐speed trains (Revival, Harmony, and Electric multiple unites (EMU)) on the Beijing–Shanghai line, and the first‐ and second‐class seats on these three trains are selected as research objects to analyse the travel selection behaviour of Chinese high‐speed train passengers. Three methods are selected to study this behaviour: the Support Vector Machine, Nested Logit model, and Multiple Logit model. The results of these three models are calibrated using LIBSVM software and STATA software and show that age, funding source, income level, and purpose of travel are major factors that affect Chinese railway passengers' choices on high‐speed railways. Finally, it is shown that the Support Vector Machine is the most accurate of these three methods, followed by the Nested Logit model. The results of this study can complement existing research on the travel selection behaviour of Chinese railway passengers and has important implications for enabling China's high‐speed rail operators to adjust their passenger transportation products.     

关于网约车订单分配策略的综述

网约车在人们的日常出行生活中扮演着非常重要的角色,随着时代的发展,越来越多的人习惯于利用手机通过出行平台打车,但有时存在乘客的请求长时间得不到满足、司机长距离空载等现象,这不仅严重地影响了乘客和司机的体验感,还降低了人们的出行效率。如何更好地匹配乘客请求和空载司机需求,一直是出行平台关注和研究的重点问题。对网约车订单分配策略的研究,有助于减少乘客等待时间,提高司机收益,减少司机空载距离,提高资源利用率。首先简述了从乘客发起打车请求到请求订单被响应的完整流程;其次,详细地介绍了在不同派单模式下的订单分配策略;最后,全面地列举了衡量订单分配策略的评估指标。     

出租车载客点个性化推荐算法

不同的出租车司机在寻找乘客选取载客点时会有不同倾向,利用三种推荐算法对上海出租车司机载客点选取行为进行分析,根据司机对载客点的喜好程度进行个性化推荐.首先,利用基于用户和基于项目的协同过滤的算法来对出租车司机的载客点进行推荐,利用正确率指标来验证算法,实验证实了这两种算法的可行性;之后,考虑到出租车的载客行为受到时间的影响,在上述两种算法基础上增加了时间因子;最后,利用隐含因子模型（LFM）,将出租车与载客点的共现矩阵进行分解,根据分解所得矩阵进行兴趣度的分析.实验结果证明,三种方法可有效形成推荐,且LFM算法推荐准确率较高.     

公交出行最优路径搜索的有向赋权图模型

当前的公交查询系统和模型在处理多目标和多模式查询时,存在着描述困难和缺乏灵活性的问题。为此,基于有向赋权图提出了一种新的公交出行最优路径搜索模型。该模型不仅可以让用户设定可接受的最大步行距离,而且通过灵活的赋权策略利用最短路径搜索算法可以满足个性化的查询要求,尤其是在多目标查询方面具有较强的表达能力。以真实的公交数据实验表明提出的模型有效、实用。     

Motion sickness in public road transport: the effect of driver, route and vehicle

Relationships between vehicle motion and passenger sickness have been investigated in a survey of 3256 passengers travelling on 56 mainland UK bus or coach journeys. Vehicle motion was measured throughout all journeys, yielding over 110 h of six-axis coach motion data from five types of coach and 17 different drivers. Overall, 28.4% of passengers reported feelings of illness, 12.8% reported nausea and 1.7% reported vomiting during coach travel. Passenger nausea and illness ratings increased with increased exposure to lateral coach motion at low frequencies (< 0.5 Hz). Motion in other axes correlated less well with sickness, although there were some intercorrelations between the motions in the different axes. Sickness levels among passengers were greater with drivers who drove to produce higher average magnitudes of fore-and-aft and lateral vehicle motion. Nausea occurrence was greater on routes classified as being predominantly cross-country where magnitudes of lateral vehicle motion were significantly higher. Lateral motion and motion sickness increased from the front to the rear of each vehicle. No significant differences in sickness were found between the five different vehicle types used in the study. The applicability of a motion sickness dose model to these data is discussed.     

基于Spark平台城市出租车乘客出行特征分析

从海量出租车GPS轨迹数据中挖掘和分析城市出租车乘客的出行特征,可以为城市交通管理者和出租车行业管理者在城市交通规划与管理、城市交通流均衡与车辆调度等方面提供决策依据.基于Spark大数据处理分析平台,选择YARN作为资源管理调度系统,采用HDFS分布式存储系统,对出租车GPS轨迹数据进行挖掘.给出了基于Spark平台的出租车乘客出行特征的挖掘方法,包括出租车乘客出行距离分布、出租车使用时间分布及出租车出行需求.实验结果表明,基于Spark平台分析方法能够快速且准确的分析出出租车乘客出行特征.     

共享汽车系统空车调度的鲁棒优化模型

面向共享汽车系统的运营商与潜在用户,针对实现最大利润的空车调度问题,同时考虑乘客需求信息的不确定性对调度过程的影响,利用基于可调决策规则的鲁棒优化方法进行建模与求解.在共享汽车系统中,乘客的出行需求是不确定的,给出相应的不确定集合描述,将乘客的出行需求限制在一定的区间内,并灵活限制时间上的乘客需求之和,以减小模型的保守性.在此基础上引入可调决策规则,使得空车调度的策略可以根据已实现的需求进行调整,提出空车调度的鲁棒优化模型及其可解的线性规划形式.仿真实验利用真实的滴滴订单信息模拟用户使用共享汽车出行的需求,展示该模型所提出的空车调度策略(相较于确定性模型)会投入更多的费用在空车调度上,使运营商在平均意义和最差情况下均获得更大的利润并满足更多的乘客需求,表明所提出模型的鲁棒性和实用性.     

Adaptive hypertext navigation based on user goals and context

Hypertext systems allow flexible access to topics of information, but this flexibility has disadvantages. Users often become lost or overwhelmed by choices. An adaptive hypertext system can overcome these disadvantages by recommending information to users based on their specific information needs and preferences. Simple associative matrices provide an effective way of capturing these user preferences. Because the matrices are easily updated, they support the kind of dynamic learning required in an adaptive system.HYPERFLEX, a prototype of an adaptive hypertext system that learns, is described. Informal studies with HYPERFLEX clarify the circumstances under which adaptive systems are likely to be useful, and suggest that HYPERFLEX can reduce time spent searching for information by up to 40%. Moreover, these benefits can be obtained with relatively little effort on the part of hypertext authors or users.The simple models underlying HYPERFLEX's performance may offer a general and useful alternative to more sophisticated modelling techniques. Conditions under which these models, and similar adaptation techniques, might be most useful are discussed.     

User-adapted travel planning system for personalized schedule recommendation

Recently, the Internet has made a lot of services and products appear online provided by many tourism sectors. By this way, many information such as timetables, routes, accommodations, and restaurants are easily available to help travelers plan their travels. However, how to plan the most appropriate travel schedule under simultaneously considering several factors such as tourist attractions visiting, local hotels selecting, and travel budget calculation is a challenge. This gives rise to our interest in exploring the recommendation systems with relation to schedule recommendation. Additionally, the personalized concept is not implemented completely in most of travel recommendation systems. One notable problem is that they simply recommended the most popular travel routes or projects, and cannot plan the travel schedule. Moreover, the existing travel planning systems have limits in their capabilities to adapt to the changes based on users’ requirements and planning results. To tackle these problems, we develop a personalized travel planning system that simultaneously considers all categories of user requirements and provides users with a travel schedule planning service that approximates automation. A novel travel schedule planning algorithm is embedded to plan travel schedules based on users’ need. Through the user-adapted interface and adjustable results design, users can replace any unsatisfied travel unit to specific one. The feedback mechanism provides a better accuracy rate for next travel schedule to new users. An experiment was conducted to examine the satisfaction and use intention of the system. The results showed that participants who used the system with schedule planning have statistical significant on user satisfaction and use intention. We also analyzed the validity of applying the proposed algorithm to a user preference travel schedule through a number of practical system tests. In addition, comparing with other travel recommendation systems, our system had better performance on the schedule adjustment, personalization, and feedback giving.     

The framing of drivers' route choices when travel time information is provided under varying degrees of cognitive load

In two experiments, participants chose between staying on a main route with a certain travel time and diverting to an alternative route that could take a range of travel times. In the first experiment, travel time information was displayed on a sheet of paper to participants seated at a desk. In the second experiment, the same information was displayed in a virtual environment through which participants drove. Overall, participants were risk-averse when the average travel time along the alternative route was shorter than the certain travel time of the main route but risk-seeking when the average travel time of the alternative route was longer than the certain travel time along the main route. In the second experiment, in which cognitive load was higher, participants simplified their decision-making strategies. A simple probabilistic model describes the risk-taking behavior and the load effects. Actual or potential applications of this research include the development of efficient travel time information systems for drivers.     

面向公平保障的共乘定价与匹配算法

在共乘场景中,具有相似行程和时间安排的多名乘客一同出行,可降低出行成本、提高车辆上座率和缓解交通拥堵。现有研究忽略了共乘收费标准不统一和司机恶意竞价对乘客共乘体验的影响。在同时考虑费用约束、车辆容量约束和绕路距离约束的的情况下,提出最大化匹配结果公平性的方案,并将共乘的定价与匹配过程建模为一个两阶段的主从博弈。针对上述方案,提出了一个基于K-means++的请求划分算法,以缩小司乘匹配范围,提高匹配效率;在满足所有参与者约束的前提下,设计了基于两阶段主从博弈的迭代算法DPMA,并从理论上证明了其收敛性。在纽约出租车数据集上进行了仿真实验,通过不同的参数设置验证了DPMA的收敛性。与已有的2个算法相比,DPMA在保障司机收益的同时,在公平指数上分别提高了34.03%和24.42%。实验结果表明所设计机制可以有效避免司机间的恶意竞价,且提高了共乘匹配的公平性。