Proof of global convergence of an efficient algorithm for predicting trip generation, trip distribution, modal split and traffic assignment simultaneously on large-scale networks

In this paper, the problem that consists of the four travel choices (i.e., trip generation, trip distribution, modal split, and traffic assignment) with variable demand is deliberately formulated as a non-linear mathematical model. The associated optimality conditions are presented. A heuristic solution algorithm that adopts combined model concept was proposed. By way of supernetwork techniques, each combined model can be addressed as an “extended” traffic assignment problem and solved using any available traffic assignment solution algorithm. As compared with the traditional four-step travel demand forecasting procedure, this heuristic has advantage of considering the travelers' travel behavior throughout the solution procedure and hence more suitable to be applied to the transportation planning applications in the real world.

Methodology for evaluating the inbound policy is required as inbound tourism turned to be one of the growing industries in Japan. In this study, forecasting inbound demand model that makes up of trip generation and distribution model was used to analyze the impact of political and external factors. Visa requirement for traveling other countries were included in the trip distribution model as the political factors. The result showed that visa requirement was confirmed to be the resistance of outbound travelers. The effect of visa free policy to Chinese traveler was estimated to get another 5 % visitors to Japan.

This research involved the development of a new traffic assignment model consisting of a set of procedures for an urbanized area with a population of 172,000. Historical, social, and economic data were used as input to conventional trip generation and trip distribution models to produce a trip table for network assignment. This fixed table was divided into three trip types: external‐external trips, external‐internal trips, and internal‐internal trips. The methodology used to develop the new traffic assignment model assigned each of the trip types by varying the diversion of trips from the minimum path. External‐external trips were assigned on a minimum path routing and external‐internal trips were assigned with a slight diversion from the minimum path. Internal‐internal trips were assigned with more diversion than external‐internal trips and adjusted by utilizing iterative volume restraint and incremental link restraint.A statistical analysis indicated that assigning trips by trip types using trip diversion and volume and link restraint produces a significant improvement in the accuracy of the assigned traffic volumes.

In 2017, the newly installed Dutch government announced in its coalition agreement to introduce a distance-based heavy goods vehicle charge, similar to the charges levied in other European countries. To study the possible transport impacts, we applied available models and methods in preparation for the introduction of this truck charge in the Netherlands in 2023, in order to present decision information to the government on different aspects of the charge. In this paper we present the analysis of different implementation scenarios for a distance-based truck charge. Different behavioural responses can be expected in terms of freight transport demand, mode choice, logistic efficiency, and route choice. Many studies however lack sufficient detail to properly describe the impact of road charges for HGV on OD flows and freight traffic. In our contribution we explore the bandwidth of impacts of different pricing schemes, using strategic transport models for freight demand and traffic assignment, with detailed modal split and route choice models, ensuring a proper representation of generalsed transport costs. We explain how we use available transport models in an overarching analytical framework to make a comprehensive impact assessment of the different responses, and to decompose the impacts on the different responses. Final impacts are quantified in terms of freight demand (spatial pattern of transport flows), modal split and traffic flows (route choice, and congestion).Depending on the pricing scenario the tonne kilometres decrease by 0.4%–4.8% on average. The modal shift impacts are stronger on longer transport distances: this is explained both by the larger impact of a distance-based charge on these routes, and by higher substitution possibilities to barge or rail. The results indicate that the overall impacts of the introduction of the different charging alternatives are moderate. However, the network impacts at local level can be substantial due to the impact of re-routing of truck trips to avoid charge.

In this paper we address the problem of distributively tracking the upper or lower bound of n time-varying signals in finite-time. In detail, each agent has access to a time-varying exogenous signal, which may encode the evolution of a physical phenomenon. All the agents are required to follow the upper (lower) bound value among such output signals in a distributed fashion. We provide a protocol to solve the above problem in the case of networked agents and undirected communication, together with formal proof of convergence and estimation of an upperbound of the convergence time. We corroborate the protocol via numerical validations in a precision farming setting.

Prioritized sweeping (PS) and its variants are model-based reinforcement-learning algorithms that have demonstrated superior performance in terms of computational and experience e‐ciency in practice. This note establishes the flrst|to the best of our knowledge|formal proof of convergence to the optimal value function when they are used as planning algorithms. We also describe applications of this result to provably e‐cient model-based reinforcement learning in the PAC-MDP framework. We do not address the issue of convergence rate in the present paper.

Traditionally, the travel forecasting process has been viewed as a sequence of travel forecasts beginning with trip origin and destination frequencies followed by origin — destination choice, mode choice and then route choice. Consideration of each of these choices separately has led to the development of different models representing each dimension of the tripmaker’s choice process. The actual travel forecast, then, results from solving a sequence of separate models which have come to be known as trip generation, trip distribution, mode split and trip assignment.

Day-to-day dynamic traffic assignment with imperfect information, bounded rationality and information sharing

This study introduces the novel concept of local detouredness, i.e. detours on subsections of a route, as a new phenomenon for understanding and modelling route choice. Traditionally, Stochastic User Equilibrium (SUE) traffic assignment models have been concerned with judging the attractiveness of a route by its total route cost. However, through empirical analysis we show that considering solely the global properties of a route is insufficient. We find that it is important to consider local detouredness both when determining realistic and tractable route choice sets and when determining route choice probabilities. For example, analysis of observed route choice data shows that route usage tends to decay with local detouredness, and that there is an apparent limit on the amount of local detouredness seen as acceptable. No existing models can account for this systematically and consistently, which is the motivation for the new route choice model proposed in this paper: the Bounded Choice Model with Local Detour Threshold (BCM-LDT). The BCM-LDT model incorporates the effect of local detouredness on route choice probability, and has an in-built mechanism that assigns zero probabilities to routes violating a bound on total route costs and/or a threshold on local detouredness. Thereby, the model consistently predicts which routes are used and unused. Moreover, the probability expression is closed-form and continuous. SUE conditions for the BCM-LDT are given, and solution existence is proven. Exploiting the special structure of the problem, a novel solution algorithm is proposed where flow averaging is integrated with a modified branch-and-bound method that iteratively column-generates all routes satisfying local and global bounds. Numerical experiments are conducted on small-scale and large-scale networks, establishing that equilibrated solutions can be found and demonstrating the influence of the BCM-LDT parameters on choice set size and flow allocation.

Reinforcement learning (RL) is a widely investigated intelligent algorithm and proved to be useful in the wireless communication area. However, for optimization problems in large-scale multi-cell networks whose dimension increases exponentially, it is unrealistic to employ a conventional centralized RL algorithm and make decisions for the entire network. Multi-agent RL, which allows distribute decision-making, is expected to solve the scalability problem but with performance issues due to the unknown global information, i.e., non-stationary environment. In this paper, we propose a parameter-sharing multi-agent RL for grouping decisions of coordinated multi-point in a large-scale network, where agents jointly serve users to enhance the cell-edge service. By sharing information via parameters, our theoretical and simulation results show that parameter sharing can largely benefit the multi-agent algorithm with convergence proof and convergence speed analysis. To reduce the effect of biased local heterogeneous experience, we also propose a transfer learning method for the parameter sharing process, whose performance of transfer learning algorithms is verified by the simulation results.

The four-step transportation planning methodology—trip generation, distribution, modal split, and traffic assignment—was developed decades ago and is still largely the platform of many existing models in practice. Nevertheless, in the past decade, new approaches have been developed to augment and improve components of this platform or to seek new modeling paradigms. It is timely to take inventory of what we have so far and to provide a preview of what lies ahead. In fact, in terms of methodological advances, improving the planning process to make it more accurate and reflective of reality is one important aspect. It is often criticized that the sequential four-step planning procedure, lacking feedback between steps, comes-up short of capturing actual behavior. Another aspect in methodological advances, equally important, is to broaden the consideration to include vital elements that were ignored in the past. Planning procedures that incorporate environmental and sustainability considerations are such good examples. This special issue is the first part of two issues on this topic. This special issue focuses on recent methodological advances in urban transportation planning; the upcoming issue pertains to emerging techniques for urban transportation planning. Four papers are contained in this special issue, as summarized below. The paper by Boyce, ‘‘Is the Sequential Travel Forecasting Paradigm Counterproductive?’’ provides an overview of the historical development and applications of the sequential travel forecasting procedure. The author questions the validity of the current paradigm, pointing to the shortcomings of considering the procedure as individual steps. The author then proposes a new travel-forecasting paradigm that integrates travel choices, leading to an integrated, internally consistent travel and route choice model. The paper by Black et al., ‘‘Sustainable Urban Transportation: Performance Indicators and Some Analytical Approaches,’’ begins by stating the importance of considering sustainability in urban transport planning and the lack of analytical tools for this purpose, including the lack of appropriate sustainability indicators. The authors then propose a study framework, define sustainability indicators, and depict tools for the analysis, using Sydney, Australia as a case study for illustration purposes. EDITORIAL

A trip generation manual and database are important for transportation planners and engineers to forecast new trip generation for any new development. Nowadays, many petrol stations have fast-food restaurant outlets. However, this land use category has yet to be included in the Malaysian Trip Generation Manual. Therefore, this study attempted to develop a new trip generation model for the new category of “petrol station with convenience store and fast-food restaurant”. Significant factors influencing the trip generation were also determined. Manual vehicle counts at the selected sites were conducted for 3 h during morning, afternoon and evening peak hours. Regression analysis was used in this study to develop the model. A simple trip generation model based on the independent variable number of restaurant seats showed a greater value for the coefficient of determination, R2, compared with the independent variables gross floor area in thousand square feet and number of pumps. The multivariable trip generation model using three independent variables generated the highest R2 among all of the models but was still below a satisfactory level. Further study is needed to improve the model for this new land use category. We must ensure more accuracy in trip generation estimation for future planning and development.

In this paper, we focus on the dynamic traffic assignment (DTA) problem on large-scale expressway networks especially under the condition of traffic events (such as severe weather, large traffic accidents etc.). We formulate the expressway network DTA problem as a nonconvex optimization problem. Considering the difficulty of solving the problem, we put forward an approximate solution algorithm based on the network flow theory with high computational efficiency and strong applicability. Specifically, a machine learning approach and a series of customization strategies of network flow model (including multi-origin and multidestination split, flow discretization and parallel edges addition methods) are proposed to transform the DTA problem into a resoluble network flow problem. After that, in order to improve the operational efficiency of an expressway network after traffic events occurring, the proposed network flow based two-layer DTA model (NTDM) is separated into two parts-traffic flow limitation model based on maximum flow algorithm and traffic assignment model based on minimum cost flow algorithm. Experiments on real expressway network data in some provinces of China show that our method can make a significant 26.03% reduction in saturation rate in a particular scenario. In efficiency test, the NTDM is at least 600 times faster than the traditional analytic method in a network with thousands of nodes.

During recent years simple combined models of modal split and route choice have been applied to both small scale networks and large scale networks in the Stockholm region. This occurred 10–15 years after the development of theoretically sound algorithms for route choice equilibria (traffic equilibria) about 1975 and the formulation of combined models (market equilibria) in the late 70s and early 80s. A natural next step is to take into account also the feedback effects between trip distribution and combined modal split/route choice equilibria. Even further, the interdependencies between land-use patterns and transportation market equilibria need to be analyzed.

A combined model for time-dependent trip distribution and traffic assignment is proposed. The model assumes known time-dependent departure rates from origins and overall arrival rates at destinations, and it then seeks to estimate the origin-destination matrix according to the observed entropy value and to minimize the total system travel time. A solution algorithm is proposed based on the Dantzig-Wolfe decomposition principle and a Lagrangian relaxation approach. Computational results on a test network are also presented and discussed.