Potential of on-demand services for urban travel

On-demand mobility services are promising to revolutionise urban travel, but preliminary studies are showing that they may actually increase the total vehicle miles travelled, thereby worsening road congestion in cities. In this study, we assess the demand for on-demand mobility services in urban areas, using a stated preference survey, to understand the potential impact of introducing on-demand services on the current modal split. The survey was carried out in the Netherlands and offered respondents a choice between bike, car, public transport and on-demand services. 1,063 valid responses are analysed with a multinomial logit and a latent class choice model. By means of the latter, we uncover four distinctive groups of travellers based on the observed choice behaviour. The majority of the sample (55%) are avid cyclists and do not see on-demand mobility as an alternative for making urban trips. Two classes (27% and 9% of the sample) would potentially use on-demand services: the former is fairly time-sensitive and would thus use on-demand service if they were sufficiently fast. The latter class however is highly cost-sensitive, and would therefore use on-demand mobility primarily if it is cheap. The fourth class (9%) shows very limited potential for using on-demand services

Operating On-Demand Ride-Sharing Services

Public transit agencies are increasingly exploring mobility options to supplement their traditional rail, bus, and streetcar offerings. One such option is demand-responsive transport (DRT), “any non-fixed route system of transporting individuals that requires advanced scheduling by the customer”. DRT presents challenging design and operations problems, including fleet sizing, network design, and dispatching. In this thesis, we present optimization techniques to address operational challenges in demand responsive transit. In Chapter 2, we review the real-time dial-a-ride problem, a vehicle routing problem with pickups and deliveries, deviation, and capacity constraints, and present a dispatching algorithm, M-RTRS, which provides service guarantees, serving all customers with a small number of vehicles while minimizing wait times. In a computational study, we show that this algorithm scales to over 30,000 requests per hour, providing an effective way to support large-scale ride-sharing services in dense cities. In Chapter 3, we introduce an approach for vehicle dispatching, A-RTRS, that tightly integrates a state-of-the-art dispatching algorithm, a machine-learning model to predict zone-to-zone demand over time, and a model predictive control optimization to relocate idle vehicles. This is shown to decrease the average wait time of passengers in a computational study. In Chapter 4, we present a relocation algorithm designed to address two challenges faced when deploying a real-world real-time dial-a-ride service. The first, a lack of historic data, because in a real-world deployment, initial adoption may be slow, and thus accumulating the amount of data needed for the machine learning approach to demand prediction presented in Chapter 3 may be impractical. The second, that vehicles may be restricted in the locations that they may idle, which must be considered when relocating them. In a computational study, we show this approach yields similar average wait time decreases to A-RTRSPh.D

A distributed approach for robust, scalable, and flexible dynamic ridesharing

This dissertation provides a solution to dynamic ridesharing problem, a NP-hard optimization problem, where a fleet of vehicles move on a road network and ridesharing requests arrive continuously. The goal is to optimally assign vehicles to requests with the objective of minimizing total travel distance of vehicles and satisfying constraints such as vehicles’ capacity and time window for pick-up and drop-off locations. The dominant approach for solving dynamic ridesharing problem is centralized approach that is intractable when size of the problem grows, thus not scalable. To address scalability, a novel agent-based representation of the problem, along with a set of algorithms to solve the problem, is proposed. Besides being scalable, the proposed approach is flexible and, compared to centralized approach, more robust, i.e., vehicle agents can handle changes in the network dynamically (e.g., in case of a vehicle breakdown) without need to re-start the operation, and individual vehicle failure will not affect the process of decision-making, respectively. In the decentralized approach the underlying combinatorial optimization is formulated as a distributed optimization problem and is decomposed into multiple subproblems using spectral graph theory. Each subproblem is formulated as DCOP (Distributed Constraint Optimization Problem) based on a factor graph representation, including a group of cooperative agents that work together to take an optimal (or near-optimal) joint action. Then a min-sum algorithm is used on the factor graph to solve the DCOP. A simulator is implemented to empirically evaluate the proposed approach and benchmark it against two alternative approaches, solutions obtained by ILP (Integer Linear Programming) and a greedy heuristic algorithm. The results show that the decentralized approach scales well with different number of vehicle agents, capacity of vehicle agents, and number of requests and outperforms: (a) the greedy heuristic algorithm in terms of solution quality and (b) the ILP in terms of execution time

Spatiotemporal Big Data Analytics for Future Mobility

University of Minnesota Ph.D. dissertation. May 2019. Major: Computer Science. Advisor: Shashi Shekhar. 1 computer file (PDF); xii, 161 pages.Recent years have witnessed the explosion of spatiotemporal big data (e.g. GPS trajectories, vehicle engine measurements, remote sensing imagery, and geotagged tweets) which has a potential to transform our societies. Terabytes of earth observation data are collected every day from thousands of places across the world. Modern vehicles are increasingly equipped with rich sensors that measure hundreds of engine variables (e.g., emissions, fuel consumption, speed, etc) annotated with timestamps and location data for every second of the vehicle’s trip. According to reports by McKinsey and Cisco, leveraging such data is potentially worth hundreds of billions of dollars annually in fuel savings. Spatiotemporal big data are also enabling many modern technologies such as on-demand transportation (e.g. Uber, Lyft). Today, the on-demand economy attracts millions of consumers annually and over $50 billion in spending. Even more growth is expected with the emergence of self-driving cars. However, spatiotemporal big data are of volume, velocity, variety, and veracity that exceed the capability of common spatiotemporal data analytic techniques. My thesis investigates spatiotemporal big data analytics that address the volume and velocity challenges of spatiotemporal big data in the context of novel applications in transportation and engine science, future mobility, and the on-demand economy. The thesis proposes scalable algorithms for mining “Non-compliant Window Co-occurrence Patterns”, which allow the discovery of correlations in spatiotemporal big data with a large number of variables. Novel upper bounds were introduced for a statistical interest measure of association to efficiently prune uninteresting candidate patterns. Case studies with real world engine data demonstrated the ability of the proposed approaches to discover patterns which are of interest to engine scientists. To address the high velocity challenge, the thesis explored online optimization heuristics for matching supply and demand in an on-demand spatial service broker. The proposed algorithms maximize the matching size while also maintaining a balanced provider utilization to ensure robustness against variations in the supply-demand ratio and that providers do not drop out. Proposed algorithms were shown to outperform related work on multiple performance measures. In addition, the thesis proposed a scalable matching and scheduling algorithm for an on-demand pickup and delivery broker for moving consumers with multiple candidate delivery locations and time intervals. Extensive evaluation showed that the proposed approach yields significant computational savings without sacrificing the solution quality