Transportation network design for maximizing flow-based accessibility

Optimal transport network design for both traffic safety and risk equity considerations

Design and management of transport networks. New approaches and experiments from a European And American perspective: An introduction

How should water resources be allocated for shale gas development? An exploratory study in China

This paper investigates a stochastic Returnable Transport Items (RTI) distribution network design problem for an RTI service provider responsible for managing RTI flows under uncertain demand and returns. The objective is to determine the optimal number and location of intermediate facilities to accommodate RTI flows while making repositioning decisions across the network. A two-stage stochastic programming model is developed to minimise costs associated with network design, RTI storage, the choice of direct or indirect shipping, and the allocation of multiple transportation modes in both forward and reverse flow channels. To address this complex problem, a robust sample average approximation method is proposed, with its efficiency evaluated through statistical validation. Additionally, the stochastic model’s effectiveness is assessed by computing the expected value of perfect information and the value of stochastic solutions. Comprehensive numerical experiments, including 64 instances with varying cost and robustness coefficients, provide managerial insights. The results demonstrate that efficient RTI network design significantly impacts multi-period operational decisions and highlights the critical role of RTI depots in mitigating demand uncertainty. Furthermore, the robust stochastic optimisation approach employed delivers high-quality solutions with an optimality gap of less than 1% within reasonable computational times. This study highlights the importance of integrating robust decision-making in RTI network design, offering practical insights for managing uncertainty and optimising cost efficiency across complex RTI networks.

Multi-period fourth-party logistics network design with the temporary outsourcing service under demand uncertainty

Traffic assignment is a fundamental tool to evaluate and analyse the travel behaviour of network users in the transportation network. Although many extensions have been developed, the principle of user equilibrium (UE) is still the cornerstone for solving traffic equilibrium problems. Applications of UE in large-scale transportation networks have been largely limited due to the overwhelming computation burden. Therefore, with the recent advances in parallel computing, this paper proposes an efficient parallel-computing framework based on Map-Reduce to solve the UE problem. This Map-Reduce model provides a concise abstraction, Map and Reduce, for separable computational tasks. We incorporate this parallel programming model into the Frank–Wolfe algorithm and gradient projection algorithm to achieve efficient in-memory computations on large clusters in a fault-tolerant manner. The proposed parallel-computing algorithms are applied to large-scale transportation networks to examine its computation efficiency. This acceleration approach is found to significantly reduce the execution time.

ABSTRACTTravel demand is indeed stochastic with day-to-day travel demand variability, because of the underlying characteristic of variability of travel activities. The stochastic travel demand can be described by different probability distribution patterns and has to be considered in transport network design, as it affects travelers' routing choice behavior against stochastic travel time and eventually the traffic flow pattern on the network. However, on the other hand, because of the inaccurate or inconsistent travel demand estimation procedures, the prediction of the travel demand distribution pattern and parameters is susceptible to a certain level of uncertainty. Therefore, both the travel demand day-to-day variability and the uncertainty in terms of estimating travel demand distribution pattern and parameters have to be incorporated in the transportation network design. This study develops an approach to model network design problem with uncertain demand variability. Specifically, it is assumed that the total travel demand comprises two parts, commuters and infrequent travelers, and only the demand of infrequent travelers is stochastic. Then, the travel time reliability is introduced to depict the path choice behavior. A finite-scenario two-stage stochastic programming approach is applied to model the network design problem with uncertain demand variability. Numerical studies are performed to illustrate the model validity and efficiency of solution algorithm.

Tailpipe emissions in the road transportation system are a major source of air pollution and greenhouse gases. One of the possible approaches is to influence drivers’ routing decisions such that the emissions and fuel consumption is minimized. In order to evaluate such condition, we develop environmental traffic assignment (E-TA) models based on user equilibrium (UE) and system optimal (SO) behavioral principles. Extending the traditional travel time-based UE and SO principles to E-TA is not straightforward because, unlike travel time, the rate of emissions increases with the increase in vehicle speed beyond a certain point. The results of various TA models show a network-wide traffic control strategy in which vehicles are routed according to SO-based E-TA, can reduce system-wide emissions. However, a system in which drivers make routing decisions to minimize their own emissions (E-UE system) results in a paradoxical situation of increased individual as well as system-wide emissions.

Comparative Analysis of Transportation Network Design Problem under Stochastic Capacity

This paper is the second of two in which the system studied has a plant which consists of two integrators with a saturable control input, and the performance criterion which is to be minimized is integral-error-squared. In the first paper the known optimum controller was shown to satisfy Pontryagin's maximum principle. In the present paper further verification of the optimum system is obtained by showing that the best performance function satisfies Bellman's partial differential equation. It is found that Bellman's equation is even satisfied for points on the switching curve, Results are also given for other performance criteria.

In this paper, a combined bilevel model of urban land-use and transportation network design is formulated. In the bilevel model, the upper model is an optimal combined problem of land-use and network design, which is used to describe the decision activity of supply side; the lower model is a user-equilibrium model, which is used to represent user activity. An algorithm for the model based on sensitivity analysis is also presented, and an example is given to illustrate the model and algorithm proposed.

This study presents an optimal electricity-heat system (EHS) planning framework to promote the accommodation of wind power while considering technical, economic and environmental criteria. To this end, the integrated demand response (IDR) is introduced as a flexibility resource to complement the inherent fluctuation of renewable energy sources and modeled by using price elasticity theory. Both the timing transferring and energy substitution potentials are considered in the proposed IDR program. Incorporating the effect of IDR into the EHS planning model, a two-stage stochastic programming model can be devised, in which the optimal EHS configuration design and associated operation control techniques are found simultaneously to minimize the system's total economic and carbon-emission costs over the planning horizon. The multi-scale uncertainties arising from both long-term demand growth and operation-level variability of renewables/load demands are captured collectively by using a scenario-based method. The suggested planning approach is illustrated on a real EHS test case and the results show that it is effective in practical applications.

Synthesis of optimal stochastic control systems by the method of successive approximations: PMM vol. 43, no. 1. 1979, pp. 7–16

Symbiotic network design strategies in the presence of coexisting transportation networks

Integrated strategic and operational planning of dry port container networks in a stochastic environment