Structure and dynamics of transportation networks: Models, methods and applications

http://www.uk.sagepub.com/books/Book234882The first section discusses the static dimension (structure) and reviews how transportation networks have been de fined and analyzed with regard to their topology, geometry, morphology, and spatial structure. It presents a critical overview of main global (network level) and local (node level) measures and examines their usefulness for understanding transportation networks. The second section explores the dynamics of transportation networks, their evolution, and the properties underlying such evolutions. Each section provides a brief background of the relevant literature, concrete applications, and policy implications in various transport modes and industries, with an interdisciplinary focus. A discussion is provided evaluating the legacy of reviewed works and potential for further developments in transport studies in general

Evidence on Impact Evaluation of Road Transport Networks using Network Theory

The development of network theory has resulted in a growing understanding of the topological properties of transport networks. This has led to knowledge on how network indicators relate to the performance of a network, their wider socio-economic impacts and insights about how networks can best be extended. It has also provided transport planners with an insight into traffic flow, travel demand, centrality and connectivity of transport networks. This rapid evidence-review summarises literature (1999-2019) that have used network theory to evaluate the impact evaluation of road networks, it also presents the technological advancements in network theory. The identified studies outline the beneficial impacts of road networks on the economy, how connectivity can be improved to improve network resilience, reliability, performance and reduce maintenance costs. A number of studies describe how networks can be designed to reduce the impact on the environment. However, with the exception of only three studies i.e Kumar and Kumar (1999), Vasas and Magura et al. (2009) and Walker et al. (2013), the impacts are not quantified. The magnitude of the impact, for a particular network, is a function of the type of model used. As studies could not be found where different models have been used to assess similar impacts, it was not possible to compare numerically the impacts of different model types. Enhancements to network theory have focused on (i) developing new measures and indicators to assess connectivity, vulnerability and economic impact of transport networks, (ii) applying weightages to nodes and links to evaluate economic and ecological impacts and (iii) developing multiple layers within the network models for better spatial analysis. Recent studies have also expanded network theory and integrated it with risk modelling and probabilistic methodologies to identify vulnerable or critical elements within a given transport network

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

A game-theoretic approach for reliability evaluation of public transportation transfers with stochastic features

A game-theoretic approach based on the framework of transferable-utility cooperative games is developed to assess the reliability of transfer nodes in public transportation networks in the case of stochastic transfer times. A cooperative game is defined, whose model takes into account the public transportation system, the travel times, the transfers and the associated stochastic transfer times, and the users’ demand. The transfer stops are modeled as the players of such a game, and the Shapley value – a solution concept in cooperative game theory – is used to identify their centrality and relative importance. Theoretical properties of the model are analyzed. A two-level Monte Carlo approximation of the vector of Shapley values associated with the nodes is introduced, which is efficient and able to take into account the stochastic features of the transportation network. The performance of the algorithm is investigated, together with that of its distributed computing variation. The usefulness of the proposed approach for planners and policy makers is shown with a simple example and on a case study from the public transportation network of Auckland, New Zealand

A multi-functional simulation platform for on-demand ride service operations

On-demand ride services or ride-sourcing services have been experiencing fast development in the past decade. Various mathematical models and optimization algorithms have been developed to help ride-sourcing platforms design operational strategies with higher efficiency. However, due to cost and reliability issues (implementing an immature algorithm for real operations may result in system turbulence), it is commonly infeasible to validate these models and train/test these optimization algorithms within real-world ride sourcing platforms. Acting as a useful test bed, a simulation platform for ride-sourcing systems will be very important to conduct algorithm training/testing or model validation through trails and errors. While previous studies have established a variety of simulators for their own tasks, it lacks a fair and public platform for comparing the models or algorithms proposed by different researchers. In addition, the existing simulators still face many challenges, ranging from their closeness to real environments of ride-sourcing systems, to the completeness of different tasks they can implement. To address the challenges, we propose a novel multi-functional and open-sourced simulation platform for ride-sourcing systems, which can simulate the behaviors and movements of various agents on a real transportation network. It provides a few accessible portals for users to train and test various optimization algorithms, especially reinforcement learning algorithms, for a variety of tasks, including on-demand matching, idle vehicle repositioning, and dynamic pricing. In addition, it can be used to test how well the theoretical models approximate the simulated outcomes. Evaluated on real-world data based experiments, the simulator is demonstrated to be an efficient and effective test bed for various tasks related to on-demand ride service operations