Journal of Transportation Engineering, Part A: Systems

The railway track geometry condition is a key factor influencing the safety and comfort of train operations, and controlling the tamping cycles of railway tracks. For the scientific disposition of limited maintenance resources, railway infrastructure managers need to predict the tamping cycles based on an accurate grasp of track geometry degradation rules. Taking each 200-m track segment as a research object, the authors analyze the uncertainty and heterogeneity of track geometry degradation based on the discrete evaluation of the track geometry condition. On this basis, an improved track geometry degradation model using Weibull distributions is proposed to accurately estimate the tamping cycle of each track segment. By considering several heterogeneous factors in the model, individualized modeling for different track segments is realized. The developed model was verified by a case study of 100 track segments of the Chinese Lanxin railway line within the jurisdiction of the Lanzhou Railway Bureau. The estimation results of model parameters reflect the influence of various heterogeneity factors on the track geometry deterioration processes and the tamping cycles of track segments. The accuracy of the tamping cycle estimation results indicates that the proposed approach has significance for guiding the management of tamping maintenance of railway track segments.

Periodic inspection could be regarded as a form of insurance to check for variation in the performance of the empirical-based track degradation model. Excessive track inspection, however, hurts economic maintenance and probably occurs because of inadequate knowledge to manage uncertainty in the track degradation model. Through regularly assessing uncertainty propagation in the model parameters, the relative importance of inspection decisions can be evaluated, and this may remove the need for excessive inspection(s). Because the evaluation process has a strict time limit, (i.e., before the next inspection arrives), a simple assessment method is required. Thus, this paper introduces a semiprobabilistic method driven by the Bayes linear theory to assess uncertainty propagation in the parameters of the linear model of track-geometry degradation. Through the assessment, two quality measures, namely, partial size and bearing adjustment of expectation of prior belief, iteratively display how parametric uncertainty propagates from one inspection to the next. As a result, in general, the proposed method shows the existence of a transition point in the inspection plan, i.e., a splitting of inspection decisions into two categories: exciting, and as expected. Although track inspection is recursive, a revision is suggested for inspection decisions under the latter category. A practical use of the proposed method is presented using real data and discussed in this paper.

The time limited–parking management plan has been widely adopted in curbside parking and in terminal parking. This plan separates parking spaces into several parking groups and manages each group with a specific duration limit. By doing so, the turnover rates and the availability of convenient parking resources are increased. In this paper, a bilevel programming model is proposed for designing a time limited–parking management plan. The proposed model is to figure out the optimal duration limit for each parking group and the number of spaces in each group, by minimizing travelers’ average walking time. A solving procedure based on a genetic algorithm is presented to estimate appropriate results. To test the effectiveness of the proposed model, a case study is designed based on the transaction data and parking configuration at the Shanghai Hongqiao P7 parking garage. The estimated result reveals that the designed time-limited plans lead to more than a 25% decrease in the average walking cost by better utilizing convenient parking spaces.

Providing travelers with full ex-post travel time information from day to day can only make a disequilibrium network evolve to the user equilibrium, rather than the system-optimal or other better-off states. To make best use of the ex-post information, this paper suggests an altering information release strategy to optimize the day-to-day disequilibrium traffic evolution. According to present strategy, the ex-post travel time information is not released directly but is properly altered in advance, with altering volume falling into travelers’ memory or perception error scopes so as not to incur distrust. The problem is formulated as dynamic programming in which the classical proportional-switch adjustment process is applied to describe travelers’ day-to-day rerouting behavior against the released information. The properties of the model are analyzed sufficiently. Due to poor properties, the problem is treated as a bound constrained optimization problem, and a constrained compass search method is suggested to solve it. Numerical examples based on two networks are given to perform and characterize the current methodology.

Highway interchanges, particularly those in rural areas, offer fertile opportunities for highway-oriented land development such as gas stations, convenience stores, restaurants, and hotels. This paper presents an exploratory analysis of potential relationships between traffic patterns and highway-oriented land development in the vicinity of rural Interstate highway interchanges. Using data from 69 rural highway interchanges along two Interstate highways in Ohio, multiple regression models are developed to predict daily traffic volume and truck percentage as a function of interchange-area land development. The models reveal that the number of gas pumps and extent of dedicated truck parking at an interchange are significantly associated with interchange traffic. The results of this analysis add to a relatively small collection of literature on the specific effects of interchange-area development on traffic patterns. Highway agencies that are responsible for the design and operation of interchanges should consider the results of this analysis when evaluating potential development opportunities in the vicinity of interchanges. Proper planning and a spirit of cooperation between highway agencies and commercial developers will ensure that interchanges will continue to function in a safe and efficient manner.

Dividing a freeway into segments is a fundamental step in establishing its crash prediction model. Instead of using the common segmentation criteria that defines short segments as homogeneous as possible, this study used basic freeway segments that contain heterogeneous geometric and operational characteristics for crash modeling. Variables as cumulative curvature ($CUR$), cumulative longitudinal gradient ($ICUM$), side clearance ($SideC$), and density of traffic signs ($DenSig$) were proposed to accommodate the possible heterogeneity in these characteristics. The generalized estimating equations ($GEEs$) were used to model the yearly crash counts (2009–2012) on freeways in Liaoning, China. The modeling results showed that a GEE with autoregressive correlation structure was the best. Accordingly, the overall crash prediction model for all samples and two separate crash prediction models for a two-way four-lane subset and greater than four-lane subset were developed. From these models it could be found that explanatory variables have significant effects on crash counts except for the $ICUM$. It was also found that the increase in segment length or annual average daily traffic (AADT) could increase the number of crashes, while setting more gradual horizontal curves or widening side clearance could reduce the risk of crash occurrence. In addition, installing more traffic signs within a reasonable density range could lower the crash frequency. This study proposes a new perspective for freeway segmentation and variable preparation that can benefit the road safety practitioner. Meanwhile, analyzing the influence of uncommon variables such as the density of traffic signs on crash occurrence can also provide more insights into the cause of crashes.

Bicyclist rolling and aerodynamic resistance parameters are needed to estimate speed and energy expenditure in various travel analysis applications. These parameters have been investigated for sport and professional bicyclists, but better understanding is needed for real-world urban bicyclists. This paper describes a field coast-down test to measure bicycle resistance parameters that can be administered during traveler intercept surveys and generate representative data for advanced bicycle travel models. Mathematical models are developed that expand on past methods by accounting for varying wind and grade and allowing for increased measurement locations per test. A 12-sensor, 100-m test setup is developed, and indoor and outdoor validation tests are performed. The additional measurement locations yield higher precision than the previous three-sensor methods, but as expected, the precision of outdoor tests is lower due to inconsistent wind, grade, and riding surface. Outdoor validation tests generate rolling resistance coefficient estimates of $0.0064\pm 0.0013$ and effective frontal area estimates of $0.63\pm 0.11{\mathrm{m}}^2$. Outdoor tests in a headwind are sufficiently sensitive to identify significant changes in resistance with riding position and tire pressure and are expected to generate realistic parameter estimates for parsimonious modeling of on-road bicyclists.

This study explores a practical value for highway capacity that is able to represent stochastic capacity and optimize the performance of designed facilities. Optimal performance of roadways could be defined in terms of cost, safety, reliability, and vehicle throughput. We optimize the performance of freeway facilities using the traffic efficiency concept and maximizing vehicle throughput. Traffic efficiency, which is a combination of flow rate breakdown probability and capacity drop, is determined for one of the major freeways in Tehran, Iran. We depict flow-efficiency diagrams for freeway sections to extract the optimum capacity. Comparing the proposed optimum capacity and conventional capacity, we suggest the design value of capacity as 90% of conventional capacity values. The proposed optimum capacity in this study insures the maximum vehicle throughput in designed facilities.

A tabu search–based algorithm is developed in this study and applied for optimizing a variable speed limit (VSL) control system. A macroscopic traffic flow model is used to predict the traffic states over the prediction horizon. The objective of the VSL control is to minimize the total value of travel time and total value of speed variation on the selected freeway segments. Sensitivity analyses are conducted for the tabu search parameters. Different weight sets of the objective function are selected and tested. Solution qualities from the tabu search algorithm and sequential quadratic programming (SQP) algorithm are compared. Numerical results clearly indicate that the proposed tabu search outperforms the SQP, which is used as a benchmark. The control results also show that the VSL can effectively reduce total travel time and total speed variation at a lane drop bottleneck. The relationships between the number of VSL control segments and total travel time, total speed variation, and combined objective function value corresponding to different weight sets are also given.