Feasibility study of aerial ropeway transit in the Holy City of Makkah

The planning and implementation of a multimodal transportation system in the Holy City of Makkah to handle the huge volumes of pilgrims to the City is not a straightforward mission, as Makkah poses numerous challenges including its unique seasonal demand patterns, mountainous terrain, and limited space. Aerial ropeway transit (ART), a promising transport technology solution, could be an important component of this system. This paper presents the results of a technical and economic feasibility study to explore the potential of introducing ART service in Makkah. The study reveals that, overall, ART service on some corridors in Makkah is technically feasible, and with some necessary enforcement measures to attract ridership could become a profitable transportation investment. Specifically, the study recommends using ART for special user groups and service/emergency crews during peak seasons and opening ART to the public as a premium service during the off-peak season.     

大型国际活动交通组织规划方法综述

探讨夏季奥运会采用的交通组织规划方法,同时总结世界上最大规模的特殊活动——沙特阿拉伯麦加朝觐/副朝交通组织规划经验。每年一度的麦加朝觐已经延续几个世纪,在最近几十年人数持续增长,朝圣者已增至600万人,包括朝觐一周内300万人和斋月期间100万人。目前,这项活动是历史上规模最大且定期举行的特殊活动,其规模预计还将大幅增长。奥运会是世界上第二大特殊活动,每4年举办一次,在主办城市持续2周。通常奥运会主办城市自身的交通压力已经非常巨大,在奥运会期间同时还要额外满足4万名奥运官员和运动员以及800万名观众的出行需求。因此,奥运会交通组织规划对于保障赛事顺利开展至关重要。通过描述这两项活动的背景和交通供需特征,概述已有交通组织规划方法,旨在分析和总结大型活动交通组织规划的经验教训,探讨可供选择的规划策略。     

Learning-based framework for transit assignment modeling under information provision

The modeling of service dynamics has been the focus of recent developments in the field of transit assignment modeling. The emerging focus on dynamic service modeling requires a corresponding shift in transit demand modeling to represent appropriately the dynamic behaviour of passengers and their responses to Intelligent Transportation Systems technologies. This paper presents the theoretical development of a departure time and transit path choice model based on the Markovian Decision Process. This model is the core of the MIcrosimulation Learning-based Approach to TRansit Assignment. Passengers, while traveling, move to different locations in the transit network at different points in time (e.g. at stop, on board), representing a stochastic process. This stochastic process is partly dependent on the transit service performance and partly controlled by the transit rider’s trip choices. This can be analyzed as a Markovian Decision Process, in which actions are rewarded and hence passengers’ optimal policies for maximizing the trip utility can be estimated. The proposed model is classified as a bounded rational model, with a constant utility term and a stochastic choice rule. The model is appropriate for modeling information provision since it distinguishes between individual’s experience with the service performance and information provided about system dynamics.