Estimating demand for paratransit bus services for people with disability

公共交通机构有责任提供高效、生产性和可靠的服务，让所有公众，特别是严重依赖这种服务的残疾乘客都能积极地接受。公共交通机构及其残疾乘客面临的一个日益严峻的挑战是预测对辅助交通和按需服务的需求。辅助客运服务是一项特殊服务，残疾乘客可以在他们指定的时间要求从他们的家直接乘车到他们想要的目的地。在像多伦多这样拥有270万居民的城市，多伦多交通委员会（TTC）每天收到约13，000个辅助客运和按需服务请求。因此，TTC必须每天制定时间表，以满足所有辅助运输和按需请求。作为回应，像Giro这样的公司已经开发了软件来帮助公共交通机构准备这些日常时间表，包括在没有辅助公交车的情况下分配按需出租车服务来弥补差距。Giro开发的Hastus-OnDemand软件使用一系列优化算法，这些算法取决于一组假设，以快速生成供公共交通机构立即使用的时间表。Hastus-OnDemand软件面临的一个主要挑战是预测一天或一小时的需求。缺乏这种需求信息会增加计算时间，导致调度延迟，并可能导致依赖需求假设来达到调度问题的最佳解决方案，而由于缺乏信息，调度问题可能不正确。我将使用TTC使用Hastus时的2年存档数据-OnDemand软件进行预订，并将与Giro合作，为Hastus OnDemand软件添加一个需求预测模块。这些数据目前归Giro所有，根据一项研究和开发协议提供访问权限。 作为项目的一部分，将开发一个通用需求模型，使Giro能够将一个需求模型作为Hastus-OnDemand软件的输入，以帮助预测使用Hastus-OnDemand软件的其他地区的预期日需求。

Public transport agencies have the responsibility of providing efficient, productive, and reliable service that is perceived positively by all members of the public, especially for passengers with disabilities who are heavily reliant on this service. One growing challenge for public transport agencies and their passengers with disabilities is predicting the demand for paratransit and on-demand services. Paratransit service is a special service where passengers with disabilities may request a ride directly from their home to their desired destination at a time they specify in the day. In a city with 2.7 million residents like Toronto, the Toronto Transit Commission (TTC) receives around 13,000 daily requests for paratransit and on-demand services. As a result, the TTC must develop schedules on a daily basis to accommodate all paratransit and on-demand requests. In response, companies like Giro has developed software to help public transport agencies prepare these daily schedules, including allocating on-demand taxi services to cover the gap when no paratransit bus is available. The Hastus-OnDemand software developed by Giro works using a series of optimization algorithms that depend on a set of assumptions made to enable quick schedule generation for immediate use by public transport agencies. A major challenge that the Hastus-OnDemand software faces is the prediction of demand in a day or by the hour. The lack of such demand information can add to computation time, causing delays in scheduling, and can cause a reliance on demand assumptions to reach an optimum solution for the scheduling problem that may not be correct due to the lack of information.In this project, I will be using 2 years of archived data from the TTC when they used the Hastus-OnDemand software to do bookings and will work with Giro to add a demand prediction module to the Hastus-OnDemand software. The data is currently owned by Giro and access is provided under an agreement for research and development. A generic demand model will be developed as part of the project that will enable Giro to include a demand model as an input in the Hastus-OnDemand software to help in predicting expected daily demand in other regions where the Hastus-OnDemand software is used.