Time-dependent Origin-destination Demand Research Articles

An integrated approach of train scheduling and rolling stock circulation with skip-stopping pattern for urban rail transit lines

Integrated planning for urban rail transit (URT) systems is a significant technique in URT operation to balance service level and operating cost. It is generally oriented towards transportation efficiency and solved in a sequential way due to the complexity, but ignores the service experiences among equitable passengers and the utilization balance among equal rolling stocks. This paper aims to study the integrated planning for an oversaturated URT line, incorporating the fairness in train scheduling and workload balance in rolling stock circulation that are measured by two defined min–max indexes. A mixed-integer nonlinear programming (MINLP) model is established to formulate the integrated planning of stop planning, train scheduling and rolling stock circulation, explicitly considering time-dependent origin–destination demand, first-come-first-serve principle and rigid capacity constraints. Due to the model complexity, an iterative searching approach (ISA) is proposed to solve the model, involving adjusting stop patterns, departure times and connections at the depot terminal. The performance of the ISA is evaluated by three cases with different time lengths based on the practical data from No.8 URT line in Guangzhou. The results are compared with the practical plans and the solutions solved by the two-stage approach respectively. It shows that the ISA can not only mitigate unfairness and balance workload, but also improve transportation efficiency. We also investigate the optimization effects and application conditions of different combinations of searching strategies in the ISA. Furthermore, the study on the typical case during peak hours shows more details about the optimization effects and mechanism of the ISA, which can reach a uniform state in both service level and resource utilization.

Calibration and validation of a simulation-based dynamic traffic assignment model for a large-scale congested network

Deployment of a simulation-based dynamic traffic assignment (DTA) model requires calibration of a large number of demand and supply input parameters. In this paper, we present a data-intensive framework for deployment, calibration, and validation of a simulation-based DTA model of Melbourne, Australia as a large-scale congested network. The model consists of 55,719 links and 24,502 nodes and simulates almost 2.1 million commuters in a 4-h morning peak period. We propose a machine learning based technique to classify and calibrate the traffic flow fundamental diagrams against empirical data obtained from a large number of freeway loop detectors across the network. An optimization framework for estimating the time-dependent origin-destination (TDOD) demand is also presented. To enhance the quality of the TDOD demand estimation, we apply a departure time profiling technique to account for the spatial differences between OD pairs. The paper also demonstrates the impact of adaptive driving on the quality of the DTA calibration. A comparison between the simulated and observed link volumes over 1,250 locations across the entire network shows that the calibration procedure generates an approximately 30% improvement in the root mean square error (RMSE). The impact of pedestrians and cyclists on the vehicular traffic is also implicitly considered in the central business district (CBD) area to improve underestimation of the simulated travel times. Validation results suggest that the calibrated DTA model successfully replicates traffic patterns in the network and support the future applications of the model for various transportation operations and planning purposes.

Pareto optimal train scheduling for urban rail transit using generalized particle swarm optimization

In urban rail transport, train timetable plays a crucial role, whose quality determines the whole system’s performance to a large extent. In practical urban rail operation, two contradictive aspects—service quality and operation cost—should be considered during train scheduling. A good train timetable should achieve considerable service quality with as little operation cost as possible. Previously, many studies have been conducted specific to urban rail train scheduling, although most of them do not put enough emphasis on its multi-objective nature. In this article, therefore, Pareto optimal urban rail train scheduling which can give more instruction to practical operation is studied. First, referring to some existing studies, the problem is reasonably defined, which takes time-dependent origin–destination demand as the input and aims at minimizing the passengers’ total travel time and the number of used train stocks. Then, an efficient iteration algorithm and a valid train stock assignment procedure are designed to calculate the passengers’ total travel time and required train stock number, respectively. On that basis, the studied problem is reasonably formulated as a bi-objective optimization model and a Pareto-based particle swarm optimization procedure is designed to solve it. Finally, with two different scaled urban rail lines, the whole methodology is illustrated and the algorithm is tested.

Optimal queue placement in dynamic system optimum solutions for single origin-destination traffic networks

The Dynamic System Optimum (DSO) traffic assignment problem aims to determine a time-dependent routing pattern of travellers in a network such that the given time-dependent origin-destination demands are satisfied and the total travel time is at a minimum, assuming some model for dynamic network loading. The network kinematic wave model is now widely accepted as such a model, given its realism in reproducing phenomena such as transient queues and spillback to upstream links. An attractive solution strategy for DSO based on such a model is to reformulate as a set of side constraints apply a standard solver, and to this end two methods have been previously proposed, one based on the discretisation scheme known as the Cell Transmission Model (CTM), and the other based on the Link Transmission Model (LTM) derived from variational theory. In the present paper we aim to combine the advantages of CTM (in tracking time-dependent congestion formation within a link) with those of LTM (avoiding cell discretisation, providing a more computationally attractive with much fewer constraints). The motivation for our work is the previously-reported possibility for DSO to have multiple solutions, which differ in where queues are formed and dissipated in the network. Our aim is to find DSO solutions that optimally distribute the congestion over links inside the network which essentially eliminate avoidable queue spillbacks. In order to do so, we require more information than the LTM can offer, but wish to avoid the computational burden of CTM for DSO. We thus adopt an extension of the LTM called the Two-regime Transmission Model (TTM), which is consistent with LTM at link entries and exits but which is additionally able to accurately track the spatial and temporal formation of the congestion boundary within a link (which we later show to be a critical element, relative to LTM). We set out the theoretical background necessary for the formulation of the network-level TTM as a set of linear side constraints. Numerical experiments are used to illustrate the application of the method to determine DSO solutions avoiding spillbacks, reduce/eliminate the congestion and to show the distinctive elements of adopting TTM over LTM. Furthermore, in comparison to a fine-level CTM-based DSO method, our formulation is seen to significantly reduce the number of linear constraints while maintaining a reasonable accuracy.

Demand-Driven Train Schedule Synchronization for High-Speed Rail Lines

This paper addresses a new class of train scheduling problems with two interconnected high-speed rail lines under given and precise time-dependent origin–destination demand input. The proposed systematic schedule synchronization approach focuses on satisfying the requirements of transfer passengers from one rail line to another rail line. Aiming to minimize passenger waiting times at stations and crowding disutility in trains, a nonlinear optimization model for a single-line case is formulated to demonstrate the modeling framework of train scheduling problems. The model is then extended to a two-line case by explicitly taking into account the number of boarding and alighting passengers at the connection station. Using a state-space representation, a novel dynamic programming algorithm is designed to solve the single-line problem as a deterministic finite-state problem with sequential decisions. An integer coding-based genetic algorithm procedure is developed to solve the proposed model for general cases with two lines. A simplified real-world example illustrates that the designed schedule is beneficial to the through passengers and transfer passengers simultaneously.

Flow-Based and Density-Based Time-Dependent Demand Estimation for Congested Urban Transportation Networks

This paper compares the estimation quality of two time-dependent origin–destination demand estimation methods in congested networks. The first method uses time-dependent link flow rate observations, whereas the second uses time-dependent link density observations. The developed methods adopt a bilevel formulation framework in which the lower-level problem solves for the time-dependent link proportions by using a simulation-based dynamic traffic assignment model. The upper-level problem is in the form of a least squares error minimization program that minimizes the difference between the observations and their corresponding estimated values. The solution algorithm integrates a simulation-based dynamic traffic assignment model and a linear approximation of the least squares error formulation by using a separable programming approach. An iterative solution algorithm is developed to achieve consistency between the estimated demand and the link proportions. A set of experiments conducted with hypothetical and real-world networks examines the performance of the two methods in replicating the observed congestion pattern in these networks. Results show that the density-based demand estimation method is more suitable for demand estimation in congested networks in that the former method captures the flow breakdown and spillback phenomena associated with high congestion.

Estimating time-dependent origin–destination demand from traffic counts: extended gradient method

Time-dependent origin–destination (TDOD) demand is a key input of dynamic traffic assignment (DTA) in advanced traffic management systems. Model reliability is highly dependent on the accuracy of this information. One method to achieve TDOD demand matrices is to use a primary demand matrix and traffic volume counts in some links of a network. This paper proposes a bi-level model to correct the TDOD demand matrix. The extended gradient method (EGM) – an iterative method that minimizes the discrepancy between the counted and estimated traffic volumes – is a suggested means to solve this problem. The methodology is first tested on a small synthetic network to verify its performance. Then, it is applied to a real network to demonstrate its scalability. The results illustrate the effectiveness of this algorithm for the correction of TDOD demand matrices.

Comparison of System- and User-Optimal Stochastic Dynamic Network Design Models Using Monte Carlo Bounding Techniques

This paper compares the continuous network design problem formulations using system-optimal (SO) and user-optimal (UO) dynamic traffic assignment when the following independent stochastic parameters with known discrete probability distributions are considered: time-dependent origin–destination demands, time-varying saturation flow rates and jam density, and network improvement unit costs. These models propagate traffic according to Daganzo's cell transmission model. Two Monte Carlo bounding techniques, common random numbers (CRN) and independent random numbers (IRN) strategies, are used to solve the stochastic models. The results show that the CRN strategy outperforms IRN on a simple test network resembling a freeway corridor. The network size is sacrificed to gain higher confidence probabilistic behavior and to understand intuitively the effects of different network improvement policies. Although the findings may not necessarily be generalized, they provide interesting and insightful information. First, the SO and UO models allocate investment differently for certain budgets, and the stochastic models may lead to an erroneous solution (i.e., a bottleneck) for some budgets. Subsequently, the results of three comparison cases are discussed: (a) for the SO models, it should be more valuable to solve the stochastic than the deterministic models, but it is not always the case for the UO models; (b) the SO models appear more desirable than the UO models for single-level analysis; and (c) it should be more valuable to solve the stochastic model accounting for more randomness.

Comparison of System- and User-Optimal Stochastic Dynamic Network Design Models Using Monte Carlo Bounding Techniques

This paper compares the continuous network design problem formulations using system-optimal (SO) and user-optimal (UO) dynamic traffic assignment when the following independent stochastic parameters with known discrete probability distributions are considered: time-dependent origin-destination demands, time-varying saturation flow rates and jam density, and network improvement unit costs. These models propagate traffic according to Daganzo's cell transmission model. Two Monte Carlo bounding techniques, common random numbers (CRN) and independent random numbers (IRN) strategies, are used to solve the stochastic models. The results show that the CRN strategy outperforms IRN on a simple test network resembling a freeway corridor. The network size is sacrificed to gain higher confidence probabilistic behavior and to understand intuitively the effects of different network improvement policies. Although the findings may not necessarily be generalized, they provide interesting and insightful information. First, ...