Data-Driven Multi-step Demand Prediction for Ride-Hailing Services Using Convolutional Neural Network

Ride-hailing services are growing rapidly and becoming one of the most disruptive technologies in the transportation realm. Accurate prediction of ride-hailing trip demand not only enables cities to better understand people's activity patterns, but also helps ride-hailing companies and drivers make informed decisions to reduce deadheading vehicle miles traveled, traffic congestion, and energy consumption. In this study, a convolutional neural network (CNN)-based deep learning model is proposed for multi-step ride-hailing demand prediction using the trip request data in Chengdu, China, offered by DiDi Chuxing. The CNN model is capable of accurately predicting the ride-hailing pick-up demand at each 1-km by 1-km zone in the city of Chengdu for every 10 minutes. Compared with another deep learning model based on long short-term memory, the CNN model is 30% faster for the training and predicting process. The proposed model can also be easily extended to make multi-step predictions, which would benefit the on-demand shared autonomous vehicles applications and fleet operators in terms of supply-demand rebalancing. The prediction error attenuation analysis shows that the accuracy stays acceptable as the model predicts more steps

Development of a key performance indicator system to benchmark relative paratransit performance

The Americans with Disabilities Act of 1990 prohibits discrimination against people with disabilities. US transit agencies are therefore required to offer services to eligible customers that complement the mobility opportunities provided to the general public on fixed-route public transit. While these paratransit services are necessary and just, they represent a proportionally large cost to agencies: approximately eight times the cost per boarding compared to fixed-route bus service. To be able to identify opportunities for (cost) efficiencies, and to further improve the quality of paratransit services offered, the twenty agencies of the American Bus Benchmarking Group (ABBG) decided to benchmark their relative performance in paratransit management and operations. To ensure comparability of agencies’ performance and hence ensure the usefulness of the benchmarking program, a key performance indicator system was developed and associated data items were defined in detail. The scope of this system went beyond the data already provided to the National Transit Database, both in amount and granularity of data collected, as well as the detail of definitions. This paper describes the challenges, respective solutions, and other lessons identified during four years of paratransit benchmarking development led by Imperial College London, the ABBG facilitators. The paper provides transit agencies and authorities as well as benchmarking practitioners and academics an opportunity to apply these lessons for the further benefit of paratransit services and their customers around the U.S