Jianjun Wu

Reversible lane-based traffic network optimization with an advanced traveller information system

In the case of two-way traffic, there are two opposite-direction flows on every road and serious unsymmetrical flows exist in rush-hour periods. One of the primary methods used for handling this kind of traffic flow is the use of reversible lanes. Which and how many lanes should be adjusted are optimization problems. They can be treated as a network design problem, i.e. the optimal decision on the resource distribution of a street and highway system in response to a growing travel demand. This article studies a new form of transportation network design problem by performing the strategy of reversible lanes. In order to describe this problem, a bi-level programming model is given in which the upper level model is to make the total system cost and flow entropy minimum, while the lower level is a stochastic user equilibrium assignment with an advanced traveller information system. Finally, the numerical example shows that, by adopting the reversible lane and advanced traveller information system, the total system cost will be greatly reduced.

Complex Network Theory Applied to the Growth of Kuala Lumpur’s Public Urban Rail Transit Network

Recently, the number of studies involving complex network applications in transportation has increased steadily as scholars from various fields analyze traffic networks. Nonetheless, research on rail network growth is relatively rare. This research examines the evolution of the Public Urban Rail Transit Networks of Kuala Lumpur (PURTNoKL) based on complex network theory and covers both the topological structure of the rail system and future trends in network growth. In addition, network performance when facing different attack strategies is also assessed. Three topological network characteristics are considered: connections, clustering and centrality. In PURTNoKL, we found that the total number of nodes and edges exhibit a linear relationship and that the average degree stays within the interval [2.0488, 2.6774] with heavy-tailed distributions. The evolutionary process shows that the cumulative probability distribution (CPD) of degree and the average shortest path length show good fit with exponential distribution and normal distribution, respectively. Moreover, PURTNoKL exhibits clear cluster characteristics; most of the nodes have a 2-core value, and the CPDs of the centrality’s closeness and betweenness follow a normal distribution function and an exponential distribution, respectively. Finally, we discuss four different types of network growth styles and the line extension process, which reveal that the rail network’s growth is likely based on the nodes with the biggest lengths of the shortest path and that network protection should emphasize those nodes with the largest degrees and the highest betweenness values. This research may enhance the networkability of the rail system and better shape the future growth of public rail networks.

Analysis of Road Network Pattern Considering Population Distribution and Central Business District

This paper proposes a road network growing model with the consideration of population distribution and central business district (CBD) attraction. In the model, the relative neighborhood graph (RNG) is introduced as the connection mechanism to capture the characteristics of road network topology. The simulation experiment is set up to illustrate the effects of population distribution and CBD attraction on the characteristics of road network. Moreover, several topological attributes of road network is evaluated by using coverage, circuitness, treeness and total length in the experiment. Finally, the suggested model is verified in the simulation of China and Beijing Highway networks.

Capacity Assignment Model To Defense Cascading Failures

How to alleviate the damages of cascading failures triggered by the overload of edges/nodes is common in complex networks. To describe the whole cascading failures process from edges overloading to nodes malfunctioning and the dynamic spanning clustering with the evolvement of traffic flow, we propose a capacity assignment model by introducing an equilibrium assignment rule of flow in artificially created scale-free traffic networks. Additionally, the capacity update rule of node is given in this paper. We show that a single failed edge may undergo the cascading failures of nodes, and a small failure has the potential to trigger a global cascade. It is suggested that enhancing the capacity of node is particularly important for the design of any complex network to defense the cascading failures. Meanwhile, it has very important theoretical significance and practical application worthiness in the development of effective methods to alleviate the damage of one or some failed edges/nodes.

Effects Of Route Guidance Systems On Small-World Networks

The route guidance systems (RGS) are efficient in alleviating traffic congestion and reducing transit time of transportation networks. This paper studies the effects of RGS on performance of variably weighted small-world networks. The properties of the average shortest path length, the maximum degree, and the largest betweenness, as important indices for characterizing the network structure in complex networks, are simulated. Results show that there is an optimal guided rate of RGS to minimize the total system cost and the average shortest path length, and proper RGS can reduce the load of the node with maximum degree or largest betweenness. In addition, we found that the load distribution of nodes guided by RGS decay as the power laws which is very important for us to understand and control traffic congestion feasible.

DYNAMICS OF ROUTING MECHANISMS ON TRAFFIC NETWORKS

In this paper, we propose a simple betweenness-driven model to capture the dynamics of traffic routing choice behaviors. By comparing with two other models (degree-driven and cost-driven), it is shown that the cost-driven routing strategy is more complex and sensitive to traffic congestion. Another result indicates that the load distributions are determined by the connectivity distribution and route choice behaviors of the traffic network. The model thus provides useful insight for the design of traffic networks.

Optimizing last trains timetable in the urban rail network: social welfare and synchronization

ABSTRACT Last train timetable design is to coordinate last train services from different lines in an urban rail network for maximizing the number of transfers. It is a challenging operational research problem to balance the competing demand of two decision agents: that of the government agencies to provide the best social services with minimal government subsidy, and that of the train operating companies to minimize operating costs. A bi-level programming model is formulated for the last train timetabling problem, in which the upper level is to maximize the social service efficiency, and the lower level is to minimize the revenue loss for the operating companies. To solve this problem, a genetic algorithm combined with an active-set approach is developed. We report the optimization results on real-world cases of the Beijing subway network. The results show that the optimized last train timetable can significantly improve the transfer coordination.

Modeling and Simulating Passenger Behavior for a Station Closure in a Rail Transit Network

A station closure is an abnormal operational situation in which the entrances or exits of a rail transit station have to be closed for some time due to an unexpected incident. A novel approach is developed to estimate the impacts of the alternative station closure scenarios on both passenger behavioral choices at the individual level and passenger demand at the disaggregate level in a rail transit network. Therefore, the contributions of this study are two-fold: (1) A basic passenger behavior optimization model is mathematically constructed based on 0–1 integer programming to describe passengers’ responses to alternative origin station closure scenarios and destination station closure scenarios; this model also considers the availability of multi-mode transportation and the uncertain duration of the station closure; (2) An integrated solution algorithm based on the passenger simulation is developed to solve the proposed model and to estimate the effects of a station closure on passenger demand in a rail transit network. Furthermore, 13 groups of numerical experiments based on the Beijing rail transit network are performed as case studies with 2,074,267 records of smart card data. The comparisons of the model outputs and the manual survey show that the accuracy of our proposed behavior optimization model is approximately 80%. The results also show that our model can be used to capture the passenger behavior and to quantitatively estimate the effects of alternative closure scenarios on passenger flow demand for the rail transit network. Moreover, the closure duration and its overestimation greatly influence the individual behavioral choices of the affected passengers and the passenger demand. Furthermore, if the rail transit operator can more accurately estimate the closure duration (namely, as g approaches 1), the impact of the closure can be somewhat mitigated.

Topological Effects and Performance Optimization in Transportation Continuous Network Design

Because of the limitation of budget, in the planning of road works, increased efforts should be made on links that are more critical to the whole traffic system. Therefore, it would be helpful to model and evaluate the vulnerability and reliability of the transportation network when the network design is processing. This paper proposes a bilevel transportation network design model, in which the upper level is to minimize the performance of the network under the given budgets, while the lower level is a typical user equilibrium assignment problem. A new solution approach based on particle swarm optimization (PSO) method is presented. The topological effects on the performance of transportation networks are studied with the consideration of three typical networks, regular lattice, random graph, and small-world network. Numerical examples and simulations are presented to demonstrate the proposed model.

Designing a safe and fair network for hazmat road transportation

Abstract A hazmat transportation network design model involving two decision levels, the network regulator and the carrier, is proposed. The regulator identifies road segments that should be opened to hazmat shipments and guarantees the public safety, whereas the carriers’ expectations of minimizing the travel time are represented with a fairness constraint. By considering the travel time uncertainty, effects of traffic conditions on decisions are studied. The proposed model is tested on real scenarios of the region of Lazio containing the city of Rome. Results show that the suggested model can get a lower risk and still guarantee an acceptable carriers’ fairness.