Advancements in sensing and the Internet of Things (IoT) technologies generate a huge amount of data. Mobility on demand (MoD) service benefits from the availability of big data in the intelligent transportation system. Given the future travel demand or origin-destination (OD) flows prediction, service providers can pre-allocate unoccupied vehicles to the customers' origins of service to reduce waiting time. Traditional approaches on future travel demand and the OD flows predictions rely on statistical or machine learning methods. Inspired by deep learning techniques for image and video processing, through regarding localized travel demands as image pixels, a novel deep learning model called multi-scale convolutional long short-term memory network (MultiConvLSTM) is developed in this paper. Rather than using the traditional OD matrix which may lead to loss of geographical information, we propose a new data structure, called OD tensor to represent OD flows, and a manipulation method, called OD tensor permutation and matricization, is introduced to handle the high dimensionality features of OD tensor. MultiConvLSTM considers both temporal and spatial correlations to predict the future travel demand and OD flows. Experiments on real-world New York taxi data of around 400 million records are performed. Our results show that the MultiConvLSTM achieves the highest accuracy in both one-step and multiple-step predictions and it outperforms the existing methods for travel demand and OD flow predictions.

An efficient transportation system can substantially benefit our society, but road intersections have always been among the major traffic bottlenecks leading to traffic congestion. Appropriate traffic signal timing adapted to real-time traffic may help mitigate such traffic congestion. However, most existing traffic signal control methods require a huge amount of road information, such as vehicle positions. In this paper, we focus on a particular road intersection and aim to minimize the average waiting time. We propose a traffic signal control (TSC) system based on an end-to-end off-policy deep reinforcement learning (deep RL) agent with background removal residual networks. The agent takes real-time images at the road intersection as input. Upon sufficient training, the agent can perform (near-) optimal traffic signaling based on real-time traffic conditions. We conduct experiments on different intersection scenarios and compare various TSC methods. The experimental results show that our end-to-end deep RL approach can adapt to the dynamic traffic based on the traffic images and outperforms other TSC methods.

An Autonomous Vehicle (AV) is believed to be the next generation transport that can enhance safety and efficiency for smart mobility. In an AV-based public transportation system, the full autonomy of AVs enables high-efficiency transport services and the potential ride-sharing feature of AV system enhances the utilization. The system manages a fleet of AVs, determines their assignments to transport requests, sends instructions concerning the optimized plans, and recommends the parking locations for the unoccupied AVs. The parking location of an empty AV is crucial in the sense that rebalancing AVs to areas with high potential service demand can curtail the unnecessary waiting time for passengers. As AVs are generally electric, proper parking locations can also facilitate vehicle-to-grid (V2G) support. In this paper, we propose a joint rebalancing and V2G coordination strategy for AV-based public transportation system. We formulate the problem as an integer linear program and propose a heuristic based on Genetic Algorithm and Model Predictive Control to solve the problem in low time complexity. Extensive experiments are performed with the real taxi service data from New York City. The formulated integer linear program is solved dynamically where each problem instance contains 3 to 5 AVs and 3 to 8 requests in 30s time interval. Compared with the transport system without rebalancing, the results show that the coordination strategy is efficient and effective in reducing unnecessary waiting time for passengers while satisfying V2G support. Compared to computational time with the standard solver, the proposed heuristic dramatically reduces the computational time.

An effective intelligent transportation system is a core part of modern smart city. The Internet of Things and vehicular communication technologies facilitate rapid development of connected and autonomous vehicles (CAVs). While most studies focus on standalone CAV technologies, collective CAV control has much potential. With the connectivity and automation of CAVs, we can employ dynamic lane reversal (DLR) to optimize the travel schedules of CAVs for performance enhancement. In this paper, we propose the dynamic lane reversal-traffic scheduling management (DLR-TSM) scheme for CAVs. The system collects the travel requests from CAVs and determines their optimal schedules and routes over dynamically reversible lanes. We formulate the routing and scheduling problem on DLR as an integer linear program. To address the scaling effect, an algorithm based on alternating direction method of multipliers is designed to solve the problem in a distributed manner. We extensively evaluate the DLR-TSM and the distributed algorithm with real-world transportation data. The simulation results show that the DLR-TSM can significantly improve the travel times of CAVs and the distributed algorithm can dramatically reduce the required computational time.

Mobility on Demand transforms the way people travel in the city and facilitates real-time vehicle hiring services. Given the predicted future travel demand, service providers can coordinate their available vehicles such that they are pre-allocated to the customers' origins of service in advance to reduce waiting time. Traditional approaches on future travel demand prediction rely on statistical or machine learning methods. Advancement in sensor technology generates huge amount of data, which enables the data-driven intelligent transportation system. In this paper, inspired by deep learning techniques for image and video processing, we propose a new deep learning model, called Multi-Scale Convolutional Long Short-Term Memory (MultiConvLSTM), by considering travel demand as image pixel values. MultiConvLSTM considers both temporal and spatial correlations to predict the future travel demand. Experiments on real-world New York taxi data with around 400 million records are performed. We show that MultiConvLSTM outperforms the existing prediction methods for travel demand prediction and achieves the highest accuracy among all in both one-step and multiple-step predictions.

Multimodal transportation systems require an effective journey planner to allocate multiple passengers to transport operators. One example is mobility-as-a-service, a new mobility service that integrates various transport modes through a single platform. In such a multimodal and diverse journey planning problem, accommodating heterogeneous passengers with different and dynamic preferences can be challenging. Furthermore, passengers may behave based on experiences and expectations, in the sense that the transport experience affects their state and decision of the next transport service. Current methods of treating each journey planning optimization as a non-time varying single experience problem cannot adequately model passenger experience and memories over many journeys over time. In this paper, we model passenger experience as a Markov model where prior experiences have a transient effect on future long-term satisfaction and retention rate. As such, we formulate a multi-objective journey planning problem that considers individual passenger preferences, experiences, and memories. The proposed approach dynamically determines utility weights to obtain an optimal journey plan for individual passengers based on their status. To balance the profit received by each transport operator, we present a variant-based proportional fairness. Our experiments using real-world and synthetic datasets show that our approach enhances passenger satisfaction, compared to baseline methods. We demonstrate that the overall profit is increased by 2.3 times, resulting in a higher retention rate caused by higher satisfaction levels. Our proposed approach can facilitate the participation of transport operators and promote passenger acceptance of MaaS.