Mobility as a service, and its associated variants, has been proposed as a method to improve the sustainability of transport systems; however, most of the approaches that have been proposed so far have been unsuccessful or have worsened the situation. The work presented in this paper investigates an intermodal system that combines a ride-pooling service with a public transport network. The system is composed of a dedicated simulator that evaluates the transport scenario and an intermodal dispatcher that optimises the service according to requests, accounting for their time windows. This intermodal approach considers trips with multiple legs, for which either ride-pooling or public transport are used. This study investigates how the batch size and the early dispatching of the last leg, supported by a vehicle reservation strategy, impact diverse demand profiles that range from single-passenger to multiple-passenger requests, while also addressing the critical aspect of fleet size. The experimental setting used in this work is the metropolitan area of Barcelona; the experimentation results yield valuable insights into the functionality of the proposed intermodal system.

This paper provides a simulation and optimisation-based system to combine public transport (PT) with ride-pooling services (RP). According to the International Transport Forum (ITF), the RP could be established as a feeder of PT and included as the first or last leg of the journey with the option of transferring to/from PT in between. The system contains a dispatching core that uses an optimisation model with heuristic parameters to quickly analyse the potential permutations for each request. This topic is frequently based on simplistic modelling in the literature, and it has not been extensively tested in major urban regions. The whole metropolitan region of Barcelona is employed in this study, with a large realistic simulation model encompassing a 20 × 15 km area with a PT network of about 3000 stations and 300 route lines and nearly 114,000 traffic links. This enables for a more accurate evaluation of system performance and trip quality computation.

This paper describes the components of an intermodal dispatcher of ride pooling requests to integrate these urban mobility systems with the public transport network in an urban area, thus making possible a new intermodal system. The intermodal dispatcher makes use of a prior dispatcher, developed exclusively for conventional ride pooling systems and a method that filters out the requests that have few possibilities of being served using the public transport system, leaving them to be served directly by ride pooling vehicles. The assignments of customers to vehicles of the ride pooling system are finally determined by an integer programming model of reduced dimensions, so that it can be solved efficiently by conventional solvers as shown in the preliminary computational results included in the paper.

Dynamic traffic models require dynamic inputs, one of the main ones being the Dynamic Origin–Destinations (OD) matrices describing the variability over time of the trip patterns across the network. The Dynamic OD Matrix Estimation (DODME) is a challenging problem since no direct observations are available, and therefore one should resort to indirect estimation approaches. Among the most efficient approaches, the one that formulates the problem in terms of a bi-level optimization problem has been widely used. This formulation solves at the upper level a nonlinear optimization problem that minimizes some distance measures between observed and estimated link flow counts at certain counting stations located in a subset of links in the network, and at the lower level a traffic assignment that estimates these link flow counts assigning the current estimated matrix. The variants of this formulation differ in the analytical approaches that estimate the link flows in terms of the traffic assignment and their time dependencies. Since these estimations are based on a traffic assignment at the lower level, these analytical approaches, although numerically efficient, imply a high computational cost. The advent of ICT applications has made available new sets of traffic-related measurements enabling new approaches; under certain conditions, the data collected allows to estimate the most likely used paths, from which a de facto assignment matrix can be computed. This allows extracting empirically similar information to that provided by the dynamic traffic assignment that is used in the analytical approaches. This paper explores how to extract such information from the recorded commercial data, proposes a new constrained non-linear optimization model to solve the DODME problem, with a reduced number of variables linearly depending on network size instead of quadratically. Moreover, the bilevel iterative process and the traffic assignment need are avoided. Validation and computational results on its performance are presented.

Short driving range, limited chargers, and long charging times challenge the profitability of electric taxi operations. In this paper, a charging algorithm is developed to accompany a taxi service with online trip requests, which uses a private charging infrastructure for both slow and fast charging. The vehicle charging curves are assumed to be piece-wise linear functions. The proposed algorithm uses historical operation data to generate a pro-active planning that avoids queuing of vehicles. The algorithm is built upon three sequential, iterative, finite-horizon Mixed Integer Linear Programs. This iterative process, in which the three MILPs are solved sequentially, allows the current time-step to be optimized, while taking future time-steps into account. This is achieved by optimizing over multiple time-steps, but only implementing the current time-step in each iteration. The sequential aspect of the algorithm allows the vast amount of information over time and space to be exploited for charging trip decisions in real time, while maintaining a tractable computation time. The first level with the longest horizon is an aggregated, daily problem, that plans the charging duration required for the fleet. The second level has a horizon of up-to three hours and is an aggregated, zone-based problem for determining charger selection and empty vehicle relocations. The third level translates the outputs of the first two problems to executable decisions for individual vehicles based on their real-time location, state of charge, and assigned passengers. The first level is the most computationally expensive and is solved using Column Generation. The performance of the first two levels is then independent of the fleet size, which makes the algorithm highly scalable. A case study with travel data for the city of Barcelona is used to test the model. Results show that the proposed method can utilize the full capacity of the charging infrastructure, and improve the number of accepted requests by 14% compared to employing a naive charging rule.

The estimation of Origin to Destination (OD) matrices is still object of continuous research interest, but the complexity of the problem, the underdetermination of the problem, the many alternatives that it offers and the role that mobility patterns represented by ODs play in transport modelling, makes it an appealing research topic, namely in the domain of dynamic approaches. The availability of new traffic measurements, due to the pervasive penetration of ICT measurements offers new paths to explore. This paper provides an insight of what can be achieved when, in addition to link flow counts, travel times, coming from treated GPS traces, are considered in the formulation of the Dynamic Origin-Destination Matrix Estimation (DODME). The analysis is conducted with an extension of conventional SPSA and a new hybrid formulation combining analytical and non-analytical formulations.

In many algorithms for traffic assignment, the most time-consuming step is shortest path search between all O–D pairs. Almost unnoticed by the transport modeling community, there has been an enormous amount of research on acceleration techniques for the shortest path problem in road networks in the past decade. These techniques usually divide the problem into a relatively expensive preprocessing phase and a significantly accelerated search phase. In this paper, the recently developed customizable contraction hierarchies are used for both shortest path search and network loading in the bi-conjugate Frank–Wolfe algorithm. For the largest test network, this approach achieves a speedup by a factor of 42 compared with a straightforward implementation of Dijkstra’s algorithm.

The Dynamic OD Matrix Estimation (DODME) is a hard problem since no direct full observations are available, and therefore one should resort to indirect estimation approaches. This formulation solves at the upper level a nonlinear optimization that minimizes some distance measures between observed and estimated link flow counts at certain counting stations located in a subset of links in the network, and at the lower level a traffic assignment that estimates these link flow counts assigning the current estimated matrix. Since these estimations are based on a traffic assignment at the lower level, these analytical approaches, although numerically efficient, imply a high computational cost. The advent of ICT applications has made available new sets of traffic related measurements enabling new approaches. This research report explores how to extract such information from the recorded data.

This paper presents the Energy and Fleet module of the TRIMODE (TRansport Integrated MODel for Europe) model framework which serves to project mobility, energy consumption and emissions up to 2050 in the EU countries. TRIMODE features a modular structure which includes a network-based transport model that simulates passenger and freight transport activity, a dedicated energy and fleet model and a computable general equilibrium model. TRIMODE formulates such individual models within a common interrelated complex modelling framework. This paper presents the energy and fleet model of TRIMODE and focuses, in particular, on the novel model developments that benefit from the direct linkages with the passenger and network models and enhance the energy consumption and emissions calculation methodology. Illustrative examples demonstrate new model formulations.

This paper proposes a model of long-distance freight traffic that is suitable for transportation models covering very large areas. Three challenges are discussed in turn. First, the geographic distribution of trips not only depends on locations of production and consumption, but also on the choice between alternative logistic distribution chains and the locations of intermediate distribution centers. Second, any stage of the distribution chain may combine several modes into a multi-leg transport. Third, the large scale of the model leads to large zones, implying a significant share of intrazonal traffic. The proposed approach adapts the four-stage model by generalizing destination choice into a distribution channel model and by introducing a mode sequence choice model for multimodal transport. A simplified distance band model is applied to intrazonal traffic.

This paper presents the TRIMODE integrated model for Europe that combines the simulation of transport, economy and energy systems for the assessment of major transport infrastructure projects and policies. Within a single software platform, the TRIMODE model components include a full four stage transport model of passenger and freight movements across Europe, an energy model with dynamic vehicle fleets for all transport modes and an economy model representing the complete macroeconomic system of European countries.

Reliable transfers are an essential characteristic of successful timetables, as passengers expect seamless door-to-door mobility.Yet most current travel demand models used for strategic timetable design do not support the evaluation of transfer reliability.An indicator is proposed which values the risk of missed transfers as the expected additional door-to-door travel time.The indicator allows transfers to be classified according to the total damage which breaking it would cause to passengers' travel plans, in terms of later arrival at the final destination, and not just the extra wait time at the transfer station.The definition, calculation method, and data sources are discussed in turn.The most immediate application of the indicator is to identify high-risk transfers during strategic timetable design and apply multi-criteria comparison between timetable variants including the transfer risk in addition to more conventional indicators like travel time or number of transfers.Several additional use cases are presented, ranging from macro-economic valuation of passenger time loss within cost-benefit analyses, to estimating the amount of penalty payments by operators to passengers due to large delays, and assessing the value of infrastructure improvements from the perspective of reducing passenger risk.

This book shows how transit assignment models can be used to describe and predict the patterns of network patronage in public transport systems. It provides a fundamental technical tool that can be em

This chapter addresses the modelling of various demand and supply phenomena emerging on public transport networks: passenger information, congestion at stops and on board, and service regularity.

This chapter deals with the data that form input and output of passenger route choice models. All information about supply and demand that is relevant to passenger route choice must be captured in a formal way in order to be accessible to mathematical choice models. Over time standard conventions for this formalisation have emerged. In order to avoid repetition in Part III, they are presented once in Sect. 5.1.

This paper extends a schedule-based transit assignment model to integrate vehicle sharing systems (VSS) with or without fixed stations permitting one-way rentals. It is assumed that travelers receive information through mobile internet on vehicle location and availability and that they can use a real-time booking service. The proposed model extends the functionality of a scheduled-based transit assignment in two ways: (1) It generates intermodal route choice sets combining transit and non-transit trip legs. This functionality enables an accessibility analysis to identify od-pairs benefiting from VSS. (2) It distributes a given travel demand on the route choice set considering capacity constraints of VSS. This functionality can be applied for an impact analysis of a proposed VSS.

Traffic control performance on networks depends on the flow response to the policy adopted, which in turn contributes to determine the optimal signal settings. This paper focuses on the relationship between local and network wide traffic control policies within the combined traffic control and assignment problem. Through a full exploration of the solution space, an in depth cross comparison is performed between the well-known local policies P0 and Equisaturation, versus the global policies Maximum Throughput and Minimum Delay, to verify how the two local policies approximate the optimal settings for signalized intersections. Realistic traffic dynamics, such as congestion, multiple controllers and spillback are considered, to empirically determine the conditions under which the local policies are able to approximate global performances. After presenting the different local and global control policies, experiments are performed on simple toy networks. The complexity of the underlying network and, therefore, of the problems' boundary conditions is then increased, allowing us to showcase how the different metrics perform in different situations. Finally, conclusions on the results are drawn.

Passengers in congested transit networks need to understand capacity constraints of vehicles when network congestion prevents boarding a vehicle. A strategy concept was developed for schedule-based networks. The passenger is assumed to know the schedule, to be aware of the capacity constraints, and to realize the possibility of being unable to board a vehicle. Therefore, the passenger must plan for several what-if scenarios at each decision point of the trip. The concept of strategies in networks with capacity constraints was extended in a study. The strategy costs were defined in terms of a random variable. Properties of this random variable, mean value and variance, for example, were used to define new types of cost functions. This approach accounts for passengers' aversion to travel time variability and risk. In the literature, these ideas were originally developed for path choice models in private traffic but had not yet been applied to public transport. In the current study, the developed mean excess approach was adjusted so that it could be applied to public transport.