Journal of Transportation Engineering, Part A: Systems

This paper investigates an optimization problem regarding feeder bus network design for metro stations, based on the wide use of battery-powered electric buses. To characterize the problem, a mixed integer programming model is formulated with the objective of minimizing the total cost of the electric feeder bus system, including the user and operator investment costs. The model considers the constraints of route length, bus stop coverage, vehicle capacity, and depot size. The solution incorporates a multiserver queuing submodel with limited system capacity to explore the relationship among the bus headway, the fleet size, and the number of depot-located chargers. Then a cost-optimized planning methodology is developed to solve these three variables for a given network. To solve the network optimization problem, a genetic algorithm that includes customized operators is developed to guide the solution evolution process. Synthetic case studies are conducted to test the model and the algorithm, and a sensitivity analysis on the important input parameters follows.

The comprehensive coverage of weighing detection systems at the entry point of every highway in China in 2020 caused congestion and unsafe traffic. In this study, weigh-in-motion data on overloaded vehicles were obtained from the Department of Highway Transportation Management of Jiangsu Province, and a spatiotemporal analysis was performed by utilizing the spatial analysis function of a geographic information system. In addition, the weight of the overloaded vehicles was calculated using the overload rate. Furthermore, the clustering and concentration of the overloaded vehicles were obtained using the global spatial autocorrelation model. The kernel density estimation method was then used to identify areas with severe overloading and calculate the local probability of overloading in that area. Our results revealed that the spatial distribution of the overloading severity was mainly influenced by the per capita income, density of highways, industry type, and freight policy in a given region, and that it was mainly concentrated in transportation hubs and areas with high traffic and complex logistics (e.g., municipal and provincial boundaries). Finally, the temporal aggregation of overloading was primarily affected by the level of law enforcement and freight policy because vehicles with high overload rates were mainly concentrated between 12:00 and 4:00 a.m.

This paper proposes a multiobjective optimization approach for a preventive maintenance scheduling problem. To model this problem, an optimization model was developed by considering two objective functions: minimizing maintenance cost associated with the preventive tamping cost, machine preparation cost, and possession cost during time scheduling, as well as minimizing the total loss of remaining useful life (LRUL) over a given planning horizon. The applicability of the model was tested with the strength Pareto evolutionary algorithm II (SPEA-II) and multiobjective particle swarm optimization (MOPSO) algorithms in a case study on a section of Tehran-Mashhad line in Iran for the planning horizon of three years. Finally, a sensitivity analysis was performed on the possession cost and maximum permitted train speed. The results showed that by increasing the maintenance budget up to 47.32%, the track capacity could be increased by 25% due to increased permitted speed. Moreover, by increasing the cost associated with track possession, the preparation cost was reduced by considering the opportunistic maintenance policy through the grouping track segments.

Energy efficiency is an important research topic in rail transit operations. Energy-efficient vertical alignment is a practical consideration during the line design process. Proper vertical alignment can reduce the train running energy consumption during the entire life cycle of the operation line. Most existing studies solved such problems through heuristic algorithms; exactly how much energy efficiency the vertical alignment can bring is still an unknown question. This study presents an energy-efficient line vertical alignment optimization model with the objective of minimum energy consumption and running time deviation and proposes an exact solution approach, the Gaussian pseudospectral method (GPM), to make this exploration. The optimization problem was transformed into the description form of the optimal train control problem. GPM was used to discretize the optimal control problem into a nonlinear programming problem, and the sequential quadratic programming algorithm completed the solution. The application of China’s Guangzhou Line 18 shows that the GPM has a better effect in solving energy-efficient vertical alignment than a heuristic algorithm. The results indicate that the presented approach can effectively improve the energy efficiency of the rail transit system.

Quantifying pedestrian and bicycle traffic is important for planning, investment, and safety improvements. Traffic agencies have implemented various pedestrian/bicyclist detection systems, but the accuracy is unsatisfactory for intersections. Some studies have explored the use of media access control (MAC) address-scanning sensors such as Bluetooth and Wi-Fi scanners. However, they may suffer from low detection rates. To overcome these shortcomings, this study proposed a system based upon Bluetooth low energy (BLE) scanners. First, the feasibility was assessed by identifying the detection rate and range of BLE scanners. Evaluation experiments uncovered that the detection rate is much higher than the Bluetooth ordinary, and it is sufficiently high for traffic count studies. Moreover, the detection range could cover the whole intersection while reducing the overestimating caused by the large detection range in comparison with other MAC address–scanning sensors. A two-step framework is then proposed for identifying the pedestrians and bicyclists from stationary objects and motorized travelers using one of the popular machine-learning algorithms, one-class support vector machine. The proposed system is validated by the benchmark count data from video footage. The results show that the system can reasonably estimate the counts of pedestrians and bicyclists in a mixed-traffic environment. The average absolute percentage error is 6.35%. This study has concluded that compared to traditional Bluetooth and Wi-Fi, BLE is more suitable for estimating the counts of pedestrians and bicyclists.

Transit signal priority (TSP) is a strategy that prioritizes the movement of transit vehicles through a signalized intersection to minimize transit delay and improve travel time reliability. Although the operational benefits are usually considered the primary criteria when deploying TSP, little attention is given to the anticipated safety impacts of TSP. Few studies that examined the safety impacts of TSP indicated inconsistent results. The objective of this study was to evaluate the safety performance of TSP. An observational before-after full Bayesian with a comparison-group approach was adopted to develop crash modification factors for total, fatal and injury (FI), and property damage only (PDO) crashes. The analysis was based on 41 transit corridors in Florida. Deployment of TSP resulted in a 12%, 14%, and 18% reduction in total, FI, and PDO crashes, respectively. Overall, the study results indicate that the deployment of TSP improves safety along the corridors. The study findings provide researchers and practitioners with an effective means for quantifying the safety benefits of the TSP and conducting an economic appraisal of TSP.

Most universities manage student parking demand by grouping parking lots into zones and selling as many parking permits as possible by the zones. This paper describes this problem as the university student parking permit problem (USP3) and proposes a solution called student permit allocation methodology (SPAM). The solution approach uses historical course registration data and oncampus travel patterns, combined with the course schedule of the target semester, to predict the spatial and temporal student parking demands in the target semester. The demand distribution patterns are then used to make decisions on the number of student parking permits for sale at the different zones. The internal components of SPAM have been validated and calibrated in parts using a midsize urban commuter campus. We further apply SPAM to illustrate that spreading the class meeting times will not only flatten the student parking demand curve but also enable more permits to be sold.

In the current version of the Highway Capacity Manual (HCM-6), the equal capacity passenger car equivalency (EC-PCE) method is used to account for the effect of trucks for capacity analyses. The EC-PCEs for freeway segments were estimated using a microsimulation-based methodology where a nonlinear regression model (NLRM) with 15 model parameters was used to develop capacity adjustment factor (CAF) models using the microsimulation data as input. The objective of this paper is to introduce a simpler nonlinear regression model with 6 model parameters that can be used for the estimation of CAF values and EC-PCE values for freeway and multilane highway segments. It was found that the proposed model can readily substitute the original model with little loss in fidelity. The CAF formulae developed in this paper can be used to calculate EC-PCE values directly, obviating the need for the HCM-6 EC-PCE tables. Equally important, the simpler models provide the user with a better understanding of the trade-offs between capacity, CAF, and EC-PCE values and the parameters that affect them.

The study aims to propose an integrated travel demand and accessibility model to examine the impact of new infrastructures on accessibility for households and employment. The cumulative opportunity measure and the spatial and temporal accessibility measure are used to describe region accessibility layout. And a zonal accessibility measure is proposed to measure attractiveness of the central business district (CBD). Further, accessibility measures are obtained by various transportation modes and times of day considering travel behavior and network traffic congestion. The complete methodology is demonstrated using a case study from the state of Maryland, US. Maryland Statewide Transportation Model is used to build an integrated travel demand and accessibility model. The investment in different projects for constrained long range plan (CLRP) (known as the vision for future growth) is compared to a case without transportation improvement but still has the same growth. Analysis results show that (1) due to lack of transit facilities, car accessibility is significantly higher than transit during peak hours; (2) because of traffic congestion, car accessibility is significantly lower during peak hours than off-peak hours, while transit is the opposite due to high frequency service during peak hours; (3) transit facility improvement can not only increase accessibility but also narrow the gap in accessibility between peak and off-peak hours; and (4) the affected region of accessibility is primarily concentrated in the metropolitan area. The study results demonstrate that it is necessary to fully assess the effect of planned transportation improvements on regional accessibility using the multimeasure accessibility analysis.

Blowing and drifting snow is a major issue in cold regions. Properly designed snow fences have been proven effective in mitigating its negative impacts. Following Federal Highway Administration guidelines, this study performed benefit–cost analyses of three types of snow fences: living snow fences (LSFs), structural snow fences (SSFs), and standing corn rows (SCRs). In addition to installation and maintenance costs, farmland rental, inconvenience cost, and production reduction also were considered. The benefits considered included snow removal cost saving, travel time saving, crash reduction, and emission reduction. The net present value and benefit–cost ratio were used in the analyses. In addition, sensitivity analyses were performed to evaluate the impacts of the analysis period, segment length, number of years for plants to mature, and discount rate on the benefit–cost ratios. The results showed that the benefit–cost ratios for LSFs and SSFs are comparable. However, LSFs are preferable to SSFs because little maintenance is needed after the plants are mature and because of potential environmental benefits of LSFs that were not included in the study. Although SCRs have the highest benefit–cost ratio, the need to renew the agency–landowner agreement annually and the alternating of crops planted may limit their snow-control effectiveness and large-scale implementation. The results indicate that installing snow fences along a segment of moderate length [$<9.7\mathrm{km}$ (6.0 mi)] would achieve a more cost-effective project. For LSFs, it would be beneficial to make a long-term agreement with landowners and select suitable plant species with rapid juvenile growth rates.