A dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers

We propose a ridesharing strategy with integrated transit in which a private on-demand mobility service operator may drop off a passenger directly door-to-door, commit to dropping them at a transit station or picking up from a transit station, or to both pickup and drop off at two different stations with different vehicles. We study the effectiveness of online solution algorithms for this proposed strategy. Queueing-theoretic vehicle dispatch and idle vehicle relocation algorithms are customized for the problem. Several experiments are conducted first with a synthetic instance to design and test the effectiveness of this integrated solution method, the influence of different model parameters, and measure the benefit of such cooperation. Results suggest that rideshare vehicle travel time can drop by 40-60% consistently while passenger journey times can be reduced by 50-60% when demand is high. A case study of Long Island commuters to New York City (NYC) suggests having the proposed operating strategy can substantially cut user journey times and operating costs by up to 54% and 60% each for a range of 10-30 taxis initiated per zone. This result shows that there are settings where such service is highly warranted

Doctor of Philosophy

dissertationData-driven analytics has been successfully utilized in many experience-oriented areas, such as education, business, and medicine. With the profusion of traffic-related data from Internet of Things and development of data mining techniques, data-driven analytics is becoming increasingly popular in the transportation industry. The objective of this research is to explore the application of data-driven analytics in transportation research to improve the traffic management and operations. Three problems in the respective areas of transportation planning, traffic operation, and maintenance management have been addressed in this research, including exploring the impact of dynamic ridesharing system in a multimodal network, quantifying non-recurrent congestion impact on freeway corridors, and developing infrastructure sampling method for efficient maintenance activities. First, the impact of dynamic ridesharing in a multimodal network is studied with agent-based modeling. The competing mechanism between dynamic ridesharing system and public transit is analyzed. The model simulates the interaction between travelers and the environment and emulates travelers' decision making process with the presence of competing modes. The model is applicable to networks with varying demographics. Second, a systematic approach is proposed to quantify Incident-Induced Delay on freeway corridors. There are two particular highlights in the study of non-recurrent congestion quantification: secondary incident identification and K-Nearest Neighbor pattern matching. The proposed methodology is easily transferable to any traffic operation system that has access to sensor data at a corridor level. Lastly, a high-dimensional clustering-based stratified sampling method is developed for infrastructure sampling. The stratification process consists of two components: current condition estimation and high-dimensional cluster analysis. High-dimensional cluster analysis employs Locality-Sensitive Hashing algorithm and spectral sampling. The proposed method is a potentially useful tool for agencies to effectively conduct infrastructure inspection and can be easily adopted for choosing samples containing multiple features. These three examples showcase the application of data-driven analytics in transportation research, which can potentially transform the traffic management mindset into a model of data-driven, sensing, and smart urban systems. The analytic

Commuter segmentation and openness to sharing services : a Swiss case study

The transportation sector is experiencing increasing pressure from emerging megatrends. Digitalisation, individualisation and the aging of society are leading to increasing traffic, mobility demand and capacity shortages. At the same time, new mobility concepts and offers are under development. In this highly dynamic environment, decision-makers and transport planners are under pressure to react. As society requests new and more comfortable mobility services, car-as well as ridesharing are seen as a part of the solution. Tailor-made mobility services have the potential to meet customer needs and increase the acceptance and use of public transport. In order to better understand the needs, we propose a classification of the commuter society into easily distinguishable groups based on an extensive commuter survey conducted in the city of Basel, Switzerland. This classification should enable more precisely targeted policy measures that save costs and increase adoption of sustainable ways of commuting. Key parameters influencing users’ openness towards car- and ridesharing are derived through an ordinal logistic regression analysis. Together with the classification based on a cluster analysis, they serve as starting points for a sustainable transformation of the commuter environment. The paper further places the findings in context by discussing how recent trends in mobility could support acceptance of new mobility concepts. Successfully transforming today’s commuting realm requires a coordinated effort from both policy-makers and society itself, integrating new and innovative mobility solutions in a public–private form of cooperation

Synergies between app-based car-related shared mobility services for the development of more profitable business models

Purpose: Emerging shared mobility services are an opportunity for cities to reduce the number of car single trips to both improve traffic congestion and the environment. Users of shared mobility services, such as carsharing, ridesharing and singular and shared ride-hailing services, often need to be customers of more than one service to cover all their transport needs, since few mobility providers offer more than one of these services from a single platform. On the other hand, providers offering these services separately do not optimize costly resources and activities, such as the vehicles or the technology. Hence, the aim of this paper is to find synergies between the different app-based car-related shared mobility services that foster the development of new business models, to increase the profitability of these services. Design/methodology/approach: The research approach is built on the literature of car-related shared mobility services business models, supported by the review of certain outstanding services websites, and face-to-face interviews with users and drivers of these transport services. The analysis is presented by means of the Business Model Canvas methodology. Findings: Based on the synergies found, this paper suggests a few different approaches for services to share some resources and activities. Originality/value: This study identifies the common features of carsharing, ridesharing and singular and shared ride-hailing services to develop more profitable business models, based on providing the services in aggregated form, or outsourcing activities and resources. In addition, the implications of these proposals are discussed as advantages and drawbacks from a business perspectivePeer ReviewedPostprint (published version

The pivotal role of Public Transport in designing the integration of mobility services and in operating MaaS offer: the concept of Shared Mobility Centre and the experience of Arezzo

The paper identifies the emerging trends and requirements in the mobility demand and the gaps between them and the offer. The paper shows how Public Authorities and Mobility Operators should provide a seamless mobility offer able to answer to mobility demand which is becoming more flexible and varied in typologies and needs. Public Transport must be the backbone of this integrated mobility offer including conventional services for main urban axes/corridors and FTS/ridesharing services for feeder, last mile and target groups services. ITS for Public Transport are the base systems to provide MaaS and Public Transport Operators should leader MaaS initiatives. Central role in the MaaS initiative must be allocated to Shared Mobility Centre as “umbrella” platform/organization able to coordinate conventional different transport services in a seamless mobility offer (from planning to operation to back-office functionalities interesting both Operators and Authorities). The experience of MaaS activated/under development in the city of Arezzo will be the opportunity to highlight some critical factors that must be guaranteed as supporting actions for MaaS

Analysis of mobility patterns and intended use of shared mobility services in the Barcelona region

Social and economic trends have strongly changed in the last years due to the economic crisis and the evolution of technology. These factors have influenced a sharing revolution, also in the mobility sector motivated for the increasing urbanisation and environmental consciousness. The paper focuses on the intended use of shared mobility services by citizens of the metropolitan Barcelona region, relying on a quantitative analysis of their mobility patterns, behaviours, needs and expectations. Six hundred surveys with commuting travellers were conducted in order to identify the differences among customers regarding different factors, such as their age, daily trips or personal incomes. Results show clear different patterns depending on whether commuting trips are within or out of the city and a greater intended use of ridesharing, carsharing and ride-hailing services of the youngest population. Besides, data indicates that travellers do not have preferences for a single mean of transport but for the service that best meets their needs in each occasion.Postprint (published version

Multimodal Transportation with Ridesharing of Personal Vehicles

Many public transportation systems are unable to keep up with growing passenger demand as the population grows in urban areas. The slow or lack of improvement for public transportation pushes people to use private transportation modes, such as carpooling and ridesharing. However, the occupancy rate of personal vehicles has been dropping in many cities. In this paper, we describe a centralized transit system that integrates public transit and ridesharing, which matches drivers and transit riders such that the riders would result in shorter travel time using both transit and ridesharing. The optimization goal of the system is to assign as many riders to drivers as possible for ridesharing. We give an exact approach and approximation algorithms to achieve the optimization goal. As a case study, we conduct an extensive computational study to show the effectiveness of the transit system for different approximation algorithms, based on the real-world traffic data in Chicago City; the data sets include both public transit and ridesharing trip information. The experiment results show that our system is able to assign more than 60% of riders to drivers, leading to a substantial increase in occupancy rate of personal vehicles and reducing riders\u27 travel time