With technological advances, mobility has been moving from a product (i.e., traditional modes and vehicles), to a service (i.e., Mobility as a Service, MaaS). However, as observed in other fields (e.g. cloud computing resource management) we argue that mobility will evolve from a service to a resource (i.e., Mobility as a Resource, MaaR). Further, due to increasing scarcity of shared mobility spaces across traditional and emerging modes, the transition must be viewed within the critical need for ethical and equitable solutions for the traveling public (i.e., research is needed to avoid hyper-market driven outcomes for society). The evolution of mobility into a resource requires novel conceptual frameworks, technologies, processes and perspectives of analysis. A key component of the future MaaR system is the technological capacity to observe, allocate and manage (in real-time) the smallest envisionable units of mobility (i.e., atomic units of mobility capacity) while providing prioritized attention to human movement and ethical metrics related to access, consumption and impact. To facilitate research into the envisioned future system, this paper proposes initial frameworks which synthesize and advance methodologies relating to highly dynamic capacity reservation systems. Future research requires synthesis across transport network management, demand behavior, mixed-mode usage, and equitable mobility.

The objective of this study is to investigate if it is possible to reduce the operational cost of an online Demand Responsive Transportation System (DRT) by using probabilistic trip demands while leaving the optimization algorithm intact. The idea is that we use probabilistic demands in order to predict actual ones. If the prediction is accurate enough then the DRT's vehicle fleet reassigned in a better state. The innovation lies in the assumption that, given enough historical data on trip demands, the system's online nature can be reduced, resulting in a better solution (problem objective). The basic steps of the proposed methodology are: (a) Based on a real historical data set, a demand distribution probability created to describe online DRT's demands behavior. (b) During operation, for each incoming demand, create a set of additional probabilistic demands based on the distribution in (a) and calculate an initial solution. (c) Remove the probabilistic demands and optimize the solution further. (d) Comparatively analyze these solutions against those that would be produced without the use of probabilistic demands. The study revealed that using probabilistic demands improved the solutions in terms of cost (objective). Test data were recorded during an actual 30-day online DRT operation at the same location, the former municipality of Philippi in northern Greece.

In this paper, an online regret based dial-a-ride (OR-DARP) algorithm is introduced and its performance evaluated on an actual demand responsive transit (DRT) system. The innovative part of the algorithm is the design of the optimization engine. A signal communication scheme between the trip dispatcher and the algorithm is used to improve utilization of the available idle time that can then be devoted to the optimization engine. The basic concept is as follows: a. Every trip request is treated as an emergency request demanding an immediate answer, b. The optimization engine runs continuously, thereby consuming every idle time fragment unless interrupted by a new trip request. The trip data are real, and they are sourced from a DRT system operating at a municipality in northern Greece where a static dial-a-ride algorithm was used as the optimization engine. Given the fact that these trips data provide all trip details plus the show-up time (the most important feature for our study), these data are the ideal basis for an “a posteriori” evaluation of the proposed online approach. Another contribution of this paper is the identification of the critical parameters in the trade-off between benefits gained from continuing to optimize an online system versus the losses of non-served demands. This important issue when applying online algorithms has not been studied extensively in the literature so far (to the best of our knowledge).

Monitoring traffic operations in a detailed manner and at the network level is a fundamental component in optimal traffic systems control. Especially in the case of urban signalized networks, accurate and reliable systems monitoring is essential for applying a wide range of control strategies, mechanisms, and concepts, such as information provision, signal control, and optimal routing. This paper presents a discrete-time optimal control scheme for monitoring urban signalized networks, augmenting real-time traffic information with a dynamic traffic model. A detailed representation of network traffic based on the cell transmission model was dynamically calibrated with a quasi-Newton nonlinear process that could tackle optimization cases of noncontinuous variables by approximating derivatives and performing a line search. An alternative network loading assumption was tested. The loading process covering all network links’ flows relied not on complex dynamic equilibrium assumptions that could infer unnecessary estimation inaccuracies but on the combined estimation of entry flows and turning proportions at each intersection. Results from the application of the proposed framework on a realistic urban network provide encouraging evidence of its value for monitoring realistic urban traffic systems.

Vendor Managed Inventory (VMI) systems seem to be at the core of most global supply chains. This is increasingly the case for electronics and automotive parts manufactured in China and assembled in the European Union countries. The main algorithmic component of VMI systems is the Inventory Routing Problem (IRP). In this paper, the authors propose an exact algorithm for the stochastic IRP. While the IRP is a well known problem, solving its stochastic version requires the development of solution policies. Solution policies can be either reactive or proactive depending on the usage of forecasts or not. In the context of the reactive policy, also known as wait and see policy, the transhipment between a supplier and retailers as well as among retailers is proposed in order to perform the required recourse actions. The problem is mathematically formulated as a stochastic program with recourse .It is solved under the context of a reactive policy by a mixed integer linear programming model that solves the IRP exactly based on a branch and cut method and an integer programming model for the execution of the recourse actions of the transshipment actions when unsatisfied demand is revealed. Computational results demonstrate that the transshipment is a powerful recourse action which may significantly improve the overall performance of a vendor managed inventory supply chain system.

Evacuations are massive operations that create heavy travel demand on road networks some of which are experiencing major congestions even with regular traffic demand. Congestion in traffic networks during evacuations, can be eased either by supply or demand management actions. This study focuses on modeling demand management strategies of optimal departure time, optimal destination choice and optimal zone evacuation scheduling (also known as staggered evacuation) under a given fixed evacuation time assumption. The analytical models are developed for a system optimal dynamic traffic assignment problem, so that their characteristics can be studied to produce insights to be used for large-scale solution algorithms. While the first two strategies were represented in a linear programming (LP) model, evacuation zone scheduling problem inevitable included integers and resulted in a mixed integer LP (MILP) one. The dual of the LP produced an optimal assignment principle, and the nature of the MILP formulations revealed clues about more efficient heuristics. The discussed properties of the models are also supported via numerical results from a hypothetical network example.

Emergency evacuation planning is a critical component of life safety. In many hazardous events, the best option is to relocate threatened populations to safer areas, often using transit. Efficient response to natural and man-made disasters requires fast and well organized efforts, which heavily depend not only on proactive planning but also on educated dynamically made decisions. Existing approaches for addressing evacuation issues are based primarily on proactive planning. A traffic network environment, however, raises more challenges, due to its dynamic structure, making planning ahead less effective in protecting the evacuees. The work presented in this paper deals with the issue of transit assisted emergency evacuation procedures in urban clusters using a dynamic network model. The ultimate goal is to present the integration of a state-of-the-art traffic microsimulation software package with transit based emergency evacuation models, demonstrating the use of transit for emergency evacuation under various conditions.

This paper is concerned with the static dial–a–ride problem; it introduces an exact approach for the 2-vehicle problem, which then is used for designing an efficient multivehicle large scale neighborhood search heuristic. The exact algorithm is based on Psaraftis’ (2) Dynamic Programming pioneering design but computationally improved for memory management. The very large neighborhood heuristic algorithm iteratively redistributes requests between any two vehicles at a time until no more improvements cane be achieved. The computational results demonstrate the efficiency of the approach and quality of the produced solutions.

Label setting techniques are all based on Dijkstra’s condition of always scanning the node with the minimum label, which guarantees that each node will be scanned exactly once; while this condition is sufficient it is not necessary. In this paper, we discuss less restrictive conditions that allow the scanning of a node that does not have the minimum label, yet still maintaining sufficiency in scanning each node exactly once; various potential shortest path schemes are discussed, based on these conditions. Two approaches, a label setting and a flexible hybrid one are designed and implemented. The performance of the algorithms is assessed both theoretically and computationally. For comparative analysis purposes, three additional shortest path algorithms – the commonly cited in the literature – are coded and tested. The results indicate that the approaches that rely on the less restrictive optimality conditions perform substantially better for a wide range of network topologies.

This paper presents an elaborative description of a complicated problem encountered at container terminals; the intraterminal transport problem. The system, operational strategies, vehicle dispatching policies, etc. are discussed extensively. An analytical, up-to-date, review of the existing research efforts relating to intraterminal transport is presented. Existing models have been reviewed based on the a) efficiency in addressing key operational and tactical questions relating to port operations, and b) relevance and applicability to different terminal operator strategies. Operations Research techniques and broad areas that take advantage of them at container terminals are discussed. The paper concludes with a critical overview of issues to be addressed to make these models more relevant to real world applications.

Transportation problems, in terms of both passenger and freight applications, are increasingly being addressed with inter-modal solutions. This chapter discusses the problem of computing optimum paths on a network with many modes of transport and time-varying link costs and travel times, accounting for the fixed schedule modes and mode-switching delays. An efficient algorithm is introduced that computes optimum path trees from all nodes and possible discrete departure times, while accounting for travel and transfer delays, as well as differences in perceived costs associated with specific modes and transfers. The algorithm, called the time-dependent inter-modal minimum cost path (TDIMCP) algorithm, is extended to set the necessary framework for solving the problem of inter-modal routing of hazardous materials, taking into consideration both risk and cost at the transfer points and travel links. Travel and transfer risk associated with hazmat routing are incorporated into the cost calculation of the TDIMCP problem, considering both the likelihood of an incident and the consequences of that incident. The inter-modal hazmat routing algorithm is then applied to a series of scenarios on a test network to illustrate the behavior of the algorithm.

This paper introduces a Variational Inequality (VI) formulation for the time-dependent combined mode split and traffic assignment problem. Travel costs are represented by generalized cost functions and mode choices are deterministically obtained based on assignment to intermodal least cost paths without accounting for possible randomness in travelers' choices. The intermodal user equilibrium (IUE) is estimated using an inner approximation (IA) algorithm that results in a nonlinear program with linear constraints. The algorithm converges assuming continuous and monotonic path travel cost functions. The paths on multimodal networks are computed with an intermodal optimum path algorithm; a cell transmission-based simulator, enhanced to account for both automobile and transit vehicles, is used to estimate the path travel costs. A heuristic search approach is proposed and implemented in the VISTA simulation-based framework. Computational results are presented on example networks to test convergence and equilibrium.

This paper showcases the utilization of a User Equilibrium (UE) Dynamic Traffic Assignment (DTA) model in the Visual Interactive System for Transport Algorithms (VISTA) to support incident management functions. More specifically, the paper presents results from a research case study in Chicago, IL that examined the effect of incidents of varying severity on traffic operations. The VISTA model was executed under a. normal conditions, b. incident conditions without traveler information availability, and c. incident conditions assuming that users had perfect knowledge of the incident conditions and could select paths to avoid the incident location. Incidents ranging from 15 to 120 minutes in duration were simulated and the impact of incident duration on travel time and incident delay was determined. The results confirm that an effective traveler information system can ease the impacts of incident conditions on traffic operations and reduce the incident-induced delays. However, attention should be given to the potential migration effects of rerouting on the operation of neighboring facilities.

A mixed-integer linear programming formulation is proposed to solve the combined system optimal dynamic traffic assignment and signal optimization problem. Traffic conditions are modeled with the cell transmission model, a convergent numerical approximation to the hydrodynamic model of traffic flow. The formulation is suited to respond to oversaturated traffic conditions. It also can be adapted to account for turning movements, protected and permissive phases (gap acceptance), and multiple signal controller types: dynamic (traffic adaptive) and pretimed. Trials with a test network validated the formulation and achieved promising results. Specifically, dynamic signal control proved to be substantially more effective than pretimed control for incident conditions. In addition, potential benefits of rerouting vehicles in both directions of a roadway were revealed even when only one direction is closed.