Multiple Origin-destination Pairs Research Articles

RCMVis: A Visual Analytics System for Route Choice Modeling.

We present RCMVis, a visual analytics system to support interactive Route Choice Modeling analysis. It aims to model which characteristics of routes, such as distance and the number of traffic lights, affect travelers' route choice behaviors and how much they affect the choice during their trips. Through close collaboration with domain experts, we designed a visual analytics framework for Route Choice Modeling. The framework supports three interactive analysis stages: exploration, modeling, and reasoning. In the exploration stage, we help analysts interactively explore trip data from multiple origin-destination (OD) pairs and choose a subset of data they want to focus on. To this end, we provide coordinated multiple OD views with different foci that allow analysts to inspect, rank, and compare OD pairs in terms of their multidimensional attributes. In the modeling stage, we integrate a k-medoids clustering method and a path-size logit model into our system to enable analysts to model route choice behaviors from trips with support for feature selection, hyperparameter tuning, and model comparison. Finally, in the reasoning stage, we help analysts rationalize and refine the model by selectively inspecting the trips that strongly support the modeling result. For evaluation, we conducted a case study and interviews with domain experts. The domain experts discovered unexpected insights from numerous modeling results, allowing them to explore the hyperparameter space more effectively to gain better results. In addition, they gained OD- and road-level insights into which data mainly supported the modeling result, enabling further discussion of the model.

Dynamic Cooperation of Transportation and Power Distribution Networks via EV Fast Charging Stations

With the development of electric vehicles, research on the cooperation of transportation networks (TNs) and power distribution networks (PDNs) has become important. Because of practicability, most cooperation research focuses on user equilibrium assignment based on the Wardrop I principle. There is less research focusing on network cooperation involving the system optimal assignment based on Wardrop II. This research paper constructs a cooperation between dynamic system optimal (DSO) and dynamic optimal power flow (DOPF) assignments with multi-objective optimization. Based on Wardrop II, this DSO model realizes multiple origin–destination pairs, multiple tasks, and multiple vehicle types. Electric vehicle and fast charging station models are designed as the connection between both networks. The optimal result gives three scenarios: TN prior, PDN prior, and a compromise of both. DSO minimized the total travel cost and DOPF minimized the total cost of power generation. Several path choices resulted from the scenarios. Whichever scenario is chosen, an electric vehicle is assigned dispersedly for a certain time period to reduce power loss. The optimal solution is also affected by the charging power in fast charging stations. This research can be applied to logistics transportation under traffic restrictions. It offers a dynamic optimization model for transportation and power operators.

Online eco-routing for electric vehicles using combinatorial multi-armed bandit with estimated covariance

Identifying energy-efficient routes in real-time has significant implications for the energy-optimal operations of electric vehicles (EVs). This study proposes a novel model for EV online eco-routing problem, which obtains the minimal expected energy consumption paths (MECPs) for multiple origin-destination (OD) pairs simultaneously. Specifically, we formulate the routing problem as a bandit problem and solve it with online algorithms. We extend the algorithms by implementing a path elimination mechanism to reduce the candidate path set and introducing the variance and covariance of the energy consumption to reduce the uncertainties. The numerical results show that the proposed algorithms can efficiently obtain near-optimal MECPs, and the solution is significantly better than the widely used shortest trip time path algorithm (STTP) and shortest trip distance path algorithm (SDP). The variation considering link energy covariance and path elimination generates paths that save 4.1% of energy compared to the SDP and 5.4% to the STTP.

System Reliability of a Stochastic Multiple-Origin-Destination Tourism Transport Network With Tardiness

As transport is a vital component in the modern tourism industry, determining reliable-performance tourism transport networks is critical for tour operators and travel agencies. The performance of a tourism transport network in this study is investigated by evaluating system reliability, which is the probability of successfully transporting requested passengers between multiple origins and multiple destinations within a time constraint when tardiness may occur. A stochastic multiple-origin-destination tourism transport network with tardiness (SMTNT) is formulated for system reliability evaluation. Identify all minimal travel paths (MTP) and corresponding maximum tardiness vectors that successfully connect each origin–destination pair under a time constraint. System reliability is derived from all flow vectors enabling delivery via MTPs a requested number of passengers at different tardiness levels. The proposed algorithm is successful in handling a combination of time constraints in the case of tardiness with multiple origin–destination pairs. Furthermore, it helps travel agencies obtain an insight into the performance of their SMTNT. An example describing the working of the proposed algorithm with an analysis of system reliability is provided. The results of the study can be used for improving the algorithm in the future.

Time-dependent transit fare optimization with elastic and spatially distributed demand

Motivated by the lack of microeconomic models that optimize time-dependent transit fares based on realistic demand formulations, this paper presents a microeconomic model for the design of a time-dependent transit pricing scheme considering elastic and spatiotemporally distributed demand. To model the spatial distribution of demand, a transit line with multiple origin–destination pairs is considered. To model the cyclical demand fluctuations, transit operations in one day are divided into multiple time periods. In the proposed model we optimize fares, headway, vehicle capacity, and maximum fleet size, with the objective of maximizing social welfare, subject to fleet size and vehicle capacity constraints. We find time-dependent pricing could avoid cross-subsidization among travelers in different time periods. Under both pricing schemes, the time-dependent headways satisfy the same optimality condition: the total rider waiting cost equals the total fixed cost on the supplier side. We also demonstrate that both resource constraints (vehicle capacity and fleet size) can be binding in multiple time periods, unlike the usual assumption in the literature that resource constraints are binding only in the period with the highest demand. Two extensions (considering a financial constraint and a variable roundtrip time) are also investigated. The developed models can be used to facilitate the design of time-dependent pricing schemes for practical applications.

A Physarum-inspired algorithm for logistics optimization: From the perspective of effective distance

The logistics optimization problem has received immense attention in recent years. The existing optimization methods generally put forward distribution strategies based on physical distance or topological distance. Hence, they have inherent limitations on effectively optimizing the logistics network in real-life situations. In order to address these concerns, this paper proposes a novel optimization model based on the concept of effective distance. We first define the effective distance in logistics networks, and then implement the network optimization based on effective distance with a Physarum-inspired algorithm that overcomes the slow convergence rate of exact algorithms. The superiority of our proposed model is that suppliers can cooperate with each other to realize cost reduction, while products from different suppliers on each link remain differentiated. Numerical examples of a logistics network with multiple origin-destination pairs have shown that our proposed model (which considers both economies of scale and cooperation among suppliers in the distribution process) provides a reliable and effective cost minimization strategy. The computational performance of our proposed algorithm is also better than other algorithms such as the particle swarm optimization and genetic algorithm, as indicated in our experiments.

When is Selfish Routing Bad? The Price of Anarchy in Light and Heavy Traffic

This paper examines the behavior of the price of anarchy as a function of the traac innow in nonatomic congestion games with multiple origin-destination (O/D) pairs. Empirical studies in real-world networks show that the price of anarchy is close to 1 in both light and heavy traac, thus raising the question: can these observations be justiied theoretically? We rst show that this is not always the case: the price of anarchy may remain a positive distance away from 1 for all values of the traac innow, even in simple three-link networks with a single O/D pair and smooth, convex costs. On the other hand, for a large class of cost functions (including all polynomials), the price of anarchy does converge to 1 in both heavy and light traac, irrespective of the network topology and the number of O/D pairs in the network. We also examine the rate of convergence of the price of anarchy, and we show that it follows a power law whose degree can be computed explicitly when the network's cost functions are polynomials.

A multi-product maximin hazmat routing-location problem with multiple origin-destination pairs

Abstract The United Nations define Supply Chain Sustainability as “the management of environmental, social and economic impacts, and the encouragement of good governance practices throughout the lifecycles of goods and services.” ( UN, 2015 ). We deal with the logistic network, an important component of the Supply Chain, in industries either generating or using hazardous material (HM) in some stage of their productive processes. A major concern when transporting HM or locating hazardous facilities is to minimize risks and hazards to the population and environment. Most studies on this issue only consider one type of HM and fail to differentiate populations with different degrees of vulnerability. This work addresses both the problem of locating hazardous facilities and routing HMs across a large, densely populated urban area, minimizing the associated costs and hazards imposed on the population. Populations, aggregated in centers, were differentiated into vulnerable populations, which are the hardest to evacuate in a short period of time, and non-vulnerable populations. Several types of HMs with different degrees of hazard were considered. We propose a multi-objective programming model with multiple origin-destination (OD) pairs that considers maximizing the minimum weighted distance between hazardous facilities and the exposed vulnerable population, maximizing the minimum weighted distance from HM transportation arcs to the exposed vulnerable population, minimizing the total hazard imposed on the non-vulnerable population, and routing and location costs. The methodology has been tested in the transportation network in the city of Santiago, Chile.

A cumulative gravity model for inter-urban spatial interaction at different scales

The classical gravity model assumes the knowledge of origin-destination (OD) flows between places. While this is reasonable for some modes of transportation such as air travel, it presents challenges to many other applications such as road travel where the observed flow on road segments, such as annual average daily traffic (AADT), may involve traffic for multiple OD pairs. This article introduces a methodology to calibrate a cumulative gravity model on a scale-specific road network. The model infers the traffic on OD pairs from the knowledge of link flows and we document its effectiveness in predicting the bidirectional volume of traffic on highways in the United States. Census data and state AADT were used to calibrate both the noncumulative and cumulative gravity models in three different regions in the western US: Arizona only, 10 western US states, and 16 western US states. The road networks were built by defining nodes as population centers of >10,000 inhabitants for Arizona and >500,000 for the other two networks. A systematic method to remove junction nodes that do not satisfy population threshold requirements but are necessary to maintain network connectivity is presented. The cumulative gravity model performed better (with a R-squared value of 0.93 in Arizona) than the standard model, but the improvement, based on two goodness-of-fit metrics (Common Part of Commuters and least squares) was above 2% only in the Arizona network. Removal of commercial traffic from the data further improved the model's calibration in the 10-state network. A thresholding method that connects the cumulative to the standard gravity model reveals that most road trips in Arizona are within the 150-mile range and that this distance increases to 500 miles in both western networks. Potential future applications of the present work are discussed.

Network Capacity Maximization Using Route Choice and Signal Control With Multiple OD Pairs

In this paper, we investigate a hybrid dynamical system which incorporates flow swap process, green-time proportion swap process, and flow divergence for a general network with multiple Origin-Destination (OD) pairs and multiple routes, where flow swap process is specified in which traffic swaps from more costly to less costly input links, green-time proportion swap process is specified in which green time at each intersection swaps from less pressurized stages to more pressurized stages, flow may diverge at each intersection from one OD pair to other OD pairs. Unlike the dynamical system model, where bottleneck delays need to be intentionally constructed to yield the equilibrium flow vector and green-time proportion vector, we propose a novel control policy to fill the gap by only adjusting the green-time proportion vector. We derive a sufficient condition for the existence of equilibrium of the dynamical system under the mild constraints that 1) the travel cost function and stage pressure function should be continuous functions and 2) the flow and green-time proportion swap processes project all flow and green-time proportion vectors on the boundary of the feasible region onto itself. We derive the condition of unique equilibrium for fixed green-time proportion vector and show that with varying green-time proportion vector, the set of equilibria is a compact, non-convex set, and with the same partial derivative of travel cost function with respect to the flow and green-time proportion vectors. Finally, we prove the stability of the proposed dynamical system by using Lyapunov stability analysis.

Understanding drivers' route choice behaviours in the urban network with machine learning models

Drivers' route choice model is essential in transportation software such as navigation, fleet management, and simulation, where the random utility models (RUM) have dominated for years. The authors investigate here whether machine learning (ML) models could be applied into this field, and whether these approaches outperform the traditional models in goodness-of-fit and prediction. The application framework and data structure are proposed, where the challenging problems lie in: (i) to pool data from multiple origin–destination pairs; and (ii) to interpret results for behaviour analysis. All RUM and ML models are applied in a real network. Results suggest that the random forest, one of the ML models, has satisfying performances with acceptable computation time, making it suitable for large network and real-time analysis. This study shows that the ML models can be adopted for behaviour analysis, such as to prioritise the importance of variables, compute the elasticity, and forecast for scenarios. Future directions on combining the RUM and ML models are discussed.

Generalized route planning approach for hazardous materials transportation with equity consideration

Hazardous materials transportation should consider risk equity and transportation risk and cost. In the hazardous materials transportation process, we consider risk equity as an important condition in optimizing vehicle routing for the long-term transport of hazardous materials between single or multiple origin-destination pairs (O-D) to reduce the distribution difference of hazardous materials transportation risk over populated areas. First, a risk equity evaluation scheme is proposed to reflect the risk difference among the areas. The evaluation scheme uses standard deviation to measure the risk differences among populated areas. Second, a risk distribution equity model is proposed to decrease the risk difference among populated areas by adjusting the path frequency between O-D pairs for hazardous materials transportation. The model is converted into two sub models to facilitate decision-making, and an algorithm is provided for each sub model. Finally, we design a numerical example to verify the accuracy and rationality of the model and algorithm. The numerical example shows that the proposed model is essential and feasible for reducing the complexity and increasing the portability of the transportation process.

Microeconomic Model for Designing Public Transit Incentive Programs

The purpose of this study is to propose a microeconomic model for optimizing a government-sponsored transit incentive program, where a rider receives a certain monetary reward once a transit trip is completed. The demand pattern considered is many-to-many, which means riders travel from multiple origins to multiple destinations. Travelers are grouped by their socio-demographic characteristics and incentives can be customized to each rider group. Analytical relations are derived from the optimality conditions of the optimization problem and numerical results are conducted to illustrate some important findings. It is found that (a) the eligibility of a certain rider group for transit incentives depends on the magnitude of the cost of public funds, and (b) an increasing profit level diminishes the social welfare. The effect of cross-subsidization among multiple origin–destination pairs is also identified and analyzed.

A modified Physarum-inspired model for the user equilibrium traffic assignment problem

• A modified Physarum -inspired model for the user equilibrium problem is proposed. • Two-way traffic characteristics and multiple OD pairs are considered. • The proposed Physarum -inspired model is very efficient in terms of both the accuracy and the execution time. The user equilibrium traffic assignment principle is very important in the traffic assignment problem. Mathematical programming models are designed to solve the user equilibrium problem in traditional algorithms. Recently, the Physarum shows the ability to address the user equilibrium and system optimization traffic assignment problems. However, the Physarum model are not efficient in real traffic networks with two-way traffic characteristics and multiple origin–destination pairs. In this article, a modified Physarum -inspired model for the user equilibrium problem is proposed. By decomposing traffic flux based on origin nodes, the traffic flux from different origin–destination pairs can be distinguished in the proposed model. The Physarum can obtain the equilibrium traffic flux when no shorter path can be discovered between each origin–destination pair. Finally, numerical examples demonstrate the rationality and convergence properties of the proposed model.

The maxisum and maximin-maxisum HAZMAT routing problems

We design routes for transportation of hazardous materials (HAZMAT) in urban areas, with multiple origin-destination pairs. First, we introduce the maxisum HAZMAT routing problem, which maximizes the sum of the population-weighted distances from vulnerable centers to their closest point on the routes. Secondly, the maximin-maxisum HAZMAT routing problem trades-off maxisum versus the population-weighted distance from the route to its closest center. We propose efficient IP formulations for both NP-Hard problems, as well as a polynomial heuristic that reaches gaps below 0.54% in a few seconds on the real case in the city of Santiago, Chile.

A Bi-Objective Approach to Evaluate Highway Routing and Regulatory Strategies for Hazardous Materials Transportation

Hazardous materials (hazmat) transportation is of concern to policymakers because of the serious safety, health, and environmental risks associated with the release of hazmat. One effective approach to minimize risks associated with hazmat transport is the prohibition of hazmat transportation on higher risk links that either pose safety hazards or increased exposure by traversing densely populated areas. Because of high risk, there are multiple stakeholders involved in hazmat transportation. While shippers and carriers are directly involved in making routing decisions, regulatory agencies influence this decision by imposing routing restrictions. In this paper, we apply a bi-objective shortest path problem to evaluate routing and regulation plans for hazmat transportation. We characterize the cost objective as the shortest path between an origin and a destination. The risk objective is to minimize the risk of exposure by restricting the link with the highest risk on the best available path from an origin to a destination. We formulate the bi-objective model and apply it to a test network. Solutions consider multiple origin-destination pairs and present a non-dominated frontier to establish routing and regulatory strategies for hazmat transportation.

An ant colony system for transportation user equilibrium analysis in congested networks

In this paper we present Ant Colony System for Traffic Assignment (ACS-TA) for the solution of deterministic and stochastic user equilibria (DUE and SUE, respectively) problems. DUE and SUE are two well known transportation problems where the transportation demand has to be assigned to an underlying network (supply in transportation terminology) according to single user satisfaction rather than aiming at some global optimum. ACS-TA turns the classic ACS meta-heuristic for discrete optimization into a technique for equilibrium computation. ACS-TA can be easily adapted to take into account all aspects characterizing the traffic assignment problem: multiple origin-destination pairs, link congestion, non-separable cost link functions, elasticity of demand, multiple classes of demand and different user cost models including stochastic cost perception. Applications to different networks, including a non-separable costs case study and the standard Sioux Falls benchmark, are reported. Results show good performance and wider applicability with respect to conventional approaches especially for stochastic user equilibrium computation.

Locating Flow-Recharging Stations at Tourist Destinations to Serve Recreational Travelers

ABSTRACT The study develops a flow-recharging location model for economically siting recharging stations based on multiple origin-destination pairs with vehicle fleets of different sizes, and the location and number of stations determined using a mixed integer program, with a case study used to validate the model. Sensitivity analyses show that a fast recharging method would significantly reduce the number of stations required, while the locating capacity has a slightly negative influence on the number. In addition, the number of each type of station determined by different recharging rates can be used to evaluate the appropriate type and deployment of such stations at the destination areas from the viewpoint of total cost.

Optimal sub-networks in traffic assignment problem and the Braess paradox

This paper is related to the Braess paradox. For a given transportation network, we are interested in the origin–destination (OD) travel costs in its sub-networks. Speaking about the performance of a network in terms of its equilibrium travel costs, we try to select the best sub-network of the original one. In a one OD pair network, by removing arcs, the equilibrium travel cost can decrease. Thus we ask for a sub-network for which the travel cost at equilibrium is minimum. In the case of multiple OD pairs, a multi-criteria comparison concept (Pareto optimality) is used to compare equilibria in sub-networks. The problem is formulated as an optimization problem. Only the fixed demand case is dealt with.

Information in road networks with multiple origin-destination pairs

In this paper the impact of information provision to travellers in a stochastic network equilibrium model with multiple origin-destination (OD) pairs is analysed. Thus far, theoretical papers addressing the same issue have been confined to the case of a single OD pair. Recent research showed that under the assumption of a single OD pair information provision is beneficial to both the informed and uninformed drivers, and hence information provision leads to a strict Pareto improvement. It might, however, be questioned whether this conclusion holds for more general networks. By constructing a network with two OD pairs results are generated that are indicative for the impact of information provision in more realistic networks. It is concluded that in networks with multiple OD paris information provision will lead to a potential Pareto improvement, but not necessarily to a strict Pareto improvement.