In a traditional transit system, passenger arrival time and bus running time are typically random and uncoordinated. This randomness gives the appearance of unbalanced passenger demand and unreliable transit services. Therefore, this paper proposes a real-time control method for bus routes. In our method, buses skip some stations and turn back at appropriate stations, in order to balance passenger demand along the bus route and improve the overall transit service. Our real-time control method considers the typical changes in passenger demand and the stochastic travel time of buses. In this paper, the number of controlled vehicles at any given time is determined, and the bus holding time at the turn-back station is adopted. When implemented correctly, the optimal scheme indicates which stations should be skipped, where it is suitable for buses to turn back, and how long the holding time should be at turn-back stations, which in turn will minimize the total cost of a transit system. This paper formulates such an integrated strategy, presents the solution method of the formulation, and proves the validity of the real-time control method.
As a major choice for daily travel, public transit plays an important role in transporting passengers, thus relieving congestion on urban transit routes. In high-demand bus networks, urban transit demand presents imbalance of use of urban bus corridors. The demand patterns in both directions are asymmetric. In this paper, we develop a model which calculates the network and transportation costs in terms of wait time, in-vehicle travel time, and operator costs. We propose an integrated strategy, with an integrated limited-stop and short-turn line, by adjusting a variety of frequencies to meet the unbalanced and asymmetric demand. To minimize these costs, a model with a genetic algorithm can determine frequencies and the proper stations which can be skipped, as well as where turning back can occur, given an origin-destination trip matrix. Numerical examples are optimized to test the availability of an integrated service by minimizing the objective function, and the results are analyzed. Our results show that integrated service patterns can be adjusted to meet the demand under different conditions. In addition, the optimized schemes of an integrated service and the frequencies derived from the model can significantly reduce total cost.
Bus bunching is one of the most serious problems of urban bus systems. Bus bunching increases waiting and travel time of passengers. Many bus systems use schedules to reach equal headways. Compared to the idea of schedules and the target headway introduced later, we propose a new method to improve the efficiency of a bus system and avoid bus bunching by boarding limits. Our solution can be effectively implemented when buses cannot travel as planned because of bad road conditions and dynamic demands at bus stops. Besides, using our method, bus headways reach the state with equal headways dynamically and spontaneously without drivers’ explicit intervention. Moreover, the method can improve the level of the bus service and reduce total travel time of passengers. We verify our method using an ideal bus route and a real bus route, both showing the success of the proposed method.
Short-term prediction of passengers' flow is one of the essential elements of the operation and real time control for public transit. Although fine prediction methodologies have been reported, they still need improvement in terms of accuracy when the current or future data either exhibit fluctuations or significant change. To address this issue, in this study, a fusion method including Kalman filtering and K-Nearest Neighbor approach is proposed. The core point of this method is to design a framework to dynamically adjust the weight coefficients of the predicted values obtained by Kalman filtering and K-Nearest Neighbor approach. The Kalman filtering and K-Nearest Neighbor approach can handle different variation trend of the data. The dynamic weight coefficient can more accurately predict the final value by giving more weight to the appropriately predicted method. In the case study of real-world data, the predicted values of alighting passengers and boarding passengers are presented by four predicted methods involving Kalman filtering, K-Nearest Neighbor approach, support vector machine, and the proposed method. According to the comparison of the test results, the proposed fusion method performed better in terms of predicting accuracy, even if time-series data abruptly varied or exhibited wide fluctuations. The proposed methodology was found as one of the effective approaches based on the historical data and current data in the area of passengers' flow forecasting for urban public transit. INDEX TERMS Short-term forecasting, urban public transit, passenger flow, fusion model.
Many intersections around the world are irregular crossings where the approach and exit lanes are offset or the two roads cross at oblique angles. These irregular intersections often confuse drivers and greatly affect operational efficiency. Although guideline markings are recommended in many design manuals and codes on traffic signs and markings to address these problems, the effectiveness and application conditions are ambiguous. The research goal was to analyze the impact of guideline markings on the saturation flow rate at signalized intersections. An adjustment estimation model was established based on field data collected at 33 intersections in Shanghai, China. The proposed model was validated using a before–after case study. The underlying reasons for the impact of intersection guideline markings on the saturation flow rate are discussed. The results reveal that the improvement in the saturation flow rate obtained from painting guide line markings is positively correlated with the number of traffic lanes, offset of through movement, and turning angle of left-turns. On average, improvements of 7.0% and 10.3% can be obtained for through and left-turn movements, respectively.
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