Proactive empty vehicle rebalancing for Demand Responsive Transport services

Worldwide, ridesharing business is steadily growing and has started to receive attention also by public transport operators. With future fleets of Autonomous Vehicles, new business models connecting schedule-based public transport and feeder fleets might become a feasible transport mode. However, such fleets require a good management to warrant a high level of service. One of the key aspects of this is proactive vehicle rebalancing based on the expected demand for trips. In this paper we model vehicle rebalancing as the Dynamic Transportation Problem. Results suggest that waiting times can be cut by around 30 % without increasing the overall vehicle miles travelled for a feeder fleet in rural Switzerland

Mode choice and ride-pooling simulation: A comparison of mobiTopp, Fleetpy, and MATSim

On-demand ride-pooling systems have gained a lot of attraction in the past years as they promise to reduce traffic and vehicle fleets compared to private vehicles. Transport simulations show that automation of vehicles and resulting fare reductions enable large-scale ride-pooling systems to have a high potential to drastically change urban transportation. For a realistic simulation of the new transport mode it is essential to model the interplay of ride-pooling demand and supply. Hence, these simulations should incorporate (1) a mode choice model to measure demand levels and (2) a dynamic model of the on-demand ride-pooling system to measure the service level and fleet performance. We compare two different simulation frameworks that both incorporate both aspects and compare their results with an identical input. It is shown that both systems are capable of generating realistic results and assessing mode choice and ride-pooling schemes. Commonalities and differences are identified and discussed

Shared autonomous vehicle services: A comprehensive review

© 2019 Elsevier Ltd The actions of autonomous vehicle manufacturers and related industrial partners, as well as the interest from policy makers and researchers, point towards the likely initial deployment of autonomous vehicles as shared autonomous mobility services. Numerous studies are lately being published regarding Shared Autonomous Vehicle (SAV) applications and hence, it is imperative to have a comprehensive outlook, consolidating the existing knowledge base. This work comprehensively consolidates studies in the rapidly emerging field of SAV. The primary focus is the comprehensive review of the foreseen impacts, which are categorised into seven groups, namely (i) Traffic & Safety, (ii) Travel behaviour, (iii) Economy, (iv) Transport supply, (v) Land–use, (vi) Environment & (vii) Governance. Pertinently, an SAV typology is presented and the components involved in modelling SAV services are described. Issues relating to the expected demand patterns and a required suitable policy framework are explicitly discussed

Self-Regulating Demand and Supply Equilibrium in Joint Simulation of Travel Demand and a Ride-Pooling Service

This paper presents the coupling of a state-of-the-art ride-pooling fleet simulation package with the mobiTopp travel demand modeling framework. The coupling of both models enables a detailed agent- and activity-based demand model, in which travelers have the option to use ride-pooling based on real-time offers of an optimized ride-pooling operation. On the one hand, this approach allows the application of detailed mode-choice models based on agent-level attributes coming from mobiTopp functionalities. On the other hand, existing state-of-the-art ride-pooling optimization can be applied to utilize the full potential of ride-pooling. The introduced interface allows mode choice based on real-time fleet information and thereby does not require multiple iterations per simulated day to achieve a balance of ride-pooling demand and supply. The introduced methodology is applied to a case study of an example model where in total approximately 70,000 trips are performed. Simulations with a simplified mode-choice model with varying fleet size (0–150 vehicles), fares, and further fleet operators’ settings show that (i) ride-pooling can be a very attractive alternative to existing modes and (ii) the fare model can affect the mode shifts to ride-pooling. Depending on the scenario, the mode share of ride-pooling is between 7.6% and 16.8% and the average distance-weighed occupancy of the ride-pooling fleet varies between 0.75 and 1.17

Customer Information Driven After Sales Service Management: Lessons from Spare Parts Logistics

Over the years, after sales service business in capital goods and high tech sectors has experienced significant growth. The drivers for growth are higher service profits, increased competitions, and primary market contractions. The enablers for growth include information driven service processes and a move from one size fit all oriented warranty contracts to service level agreement offerings that differ in service prices and response guarantees. Although, these trends provide an opportunity to the service providers to match their service resources to the time varying service requirements of a heterogeneous customer base, the tools and techniques to support decision makers are lacking as of to date. In this thesis, we aim to make a small contribution in closing this gap. We gain business environment related insights of after sales service by studying it at a major computer equipment manufacturer. After sales service is a complex task that is accomplished by making a series of strategic, tactical, and operational decisions in maintenance services management, spare parts logistics management and spare part returns management. We exclusively focus on operational and tactical decisions in spare parts logistics management. We identify that customer information, or more specifically installed base information is a valuable source to support spare parts logistics decisions at the operational and tactical levels. We present an execution technique for spare parts logistics that uses installed base information to provide differentiated service to a heterogeneous customer base and results in additional profits for the service provider. Finally, we study execution decisions in spare parts logistics and spare part returns management for their interrelation. We highlight that explicit consideration of this interrelation yields additional benefits

Estimation and optimization methods for transportation networks

While the traditional approach to ease traffic congestion has focused on building infrastructure, the recent emergence of Connected and Automated Vehicles (CAVs) and urban mobility services (e.g., Autonomous Mobility-on-Demand (AMoD) systems) has opened a new set of alternatives for reducing travel times. This thesis seeks to exploit these advances to improve the operation and efficiency of Intelligent Transportation Systems using a network optimization perspective. It proposes novel methods to evaluate the prospective benefits of adopting socially optimal routing schemes, intermodal mobility, and contraflow lane reversals in transportation networks. This dissertation makes methodological and empirical contributions to the transportation domain. From a methodological standpoint, it devises a fast solver for the Traffic Assignment Problem with Side Constraints which supports arbitrary linear constraints on the flows. Instead of using standard column-generation methods, it introduces affine approximations of the travel latency function to reformulate the problem as a quadratic (or linear) programming problem. This framework is applied to two problems related to urban planning and mobility policy: social routing with rebalancing in intermodal mobility systems and planning lane reversals in transportation networks. Moreover, it proposes a novel method to jointly estimate the Origin-Destination demand and travel latency functions of the Traffic Assignment Problem. Finally, it develops a model to jointly optimize the pricing, rebalancing and fleet sizing decisions of a Mobility-on-Demand service. Empirically, it validates all the methods by testing them with real transportation topologies and real traffic data from Eastern Massachusetts and New York City showing the achievable benefits obtained when compared to benchmarks