Investigation of Control Method for Connected-Automated Vehicle to Multiplex Dedicated Bus Lane

Dedicated bus lanes (DBLs) have been widely utilized to ensure public transport priority. To improve overall road efficiency, various control methods of multiplexing DBL are developed and discussed. In this study, we focus on the control method which is based on the connected-automated vehicle (CAV) technology, and the proposed method is validated by using microscopic traffic simulation. The simulation results show that two proposed control methods of multiplexing DBL can reduce the average delay and the average number of stops and increase the travel speed. In comparison, the real-time control method based on the CAV technology offers better effects than the improved signal light control method.

MTCS: A Method of Analyzing the Traffic Capacity of Urban Bus Lane Based on Vissim Simulation

As an effective method of improving the attractiveness of urban public transport and alleviating urban traffic congestion, bus lanes play an important role in the urban public transport system. The research on the capacity of bus lanes is conducive to improve the operation efficiency of urban bus roads and improve the service level of urban public transport. To obtain the maximum capacity of the bus lane, on one hand, the empirical formula can be used for theoretical calculation, and on the other hand, the simulation model can be established for analysis and verification. Based on the idea of simulation, a method using Vissim is proposed, called MTCS (Minimum Traffic Capacity Substitution Method). The method divides the bus lane into different sections by intersections and stops, establishes simulation model of the bus lane to calculate the traffic capacity of each section such as vehicle speed and flow and select the minimum traffic capacity of the sections as the traffic capacity of the bus lane, which is verified by using the road saturation. The simulation process uses the actual travel speed and traffic flow of the bus lane as evaluation indicators, with the aim of maximizing the road traffic flow while the actual speed of vehicles on the road is close to the desired speed, thus achieving the desired road traffic state. To verify and improve the effectiveness of the method, its analysis results are compared with the empirical formula, and various methods of enhancing traffic capacity are quantitatively simulated. The parameters of the simulation model are set by the actual bus lane example, and the experimental results show that by the methods of modifying the stop-station mode and the signal-lamp cycle, 10% and 14% improvements can be achieved, respectively. This has a good reference value for the construction of bus lanes and the adjustment of road facilities.

Parameter Sensitivity Analysis of a Cooperative Dynamic Bus Lane System With Connected Vehicles

This paper aims to evaluate the sensitivity of the proposed cooperative dynamic bus lane system with microscopic traffic simulation models. The system creates a flexible bus priority lane that is only activated on demand at an appropriate time with advanced information and communication technologies, which can maximize the use of road space. A decentralized multi-lane cooperative algorithm is developed and implemented in a microscopic simulation environment to coordinate lane changing, gap acceptance, and car-following driving behavior for the connected vehicles (CVs) on the bus lane and the adjacent lanes. The key parameters for the sensitivity study include the penetration rate and communication range of CVs, considering the transition period and gradual uptake of CVs. Multiple scenarios are developed and compared to analyze the impact of key parameters on the system’s performance, such as total saved travel time of all passengers and travel time variation among buses and private vehicles. The microscopic simulation models showed that the cooperative dynamic bus lane system is significantly sensitive to the variations of the penetration rate and the communication range in a congested traffic state. With a CV system and a communication range of 150 m, buses obtain maximum benefits with minimal impacts on private vehicles in the study simulation. The safety concerns induced by cooperative driving behavior are also discussed in this paper.

An Influence Analytical Model of Dedicated Bus Lane on Network Traffic by Macroscopic Fundamental Diagram

To study the influence mechanism of dedicated bus lanes on the urban road network, this paper proposes a novel analytical model of macroscopic fundamental diagram (MFD) and passenger macroscopic fundamental diagram (p-MFD) and the corresponding indicators based on MFD and p-MFD to evaluate the operation of the network. Taking the grid network as an example, this paper collects traffic flow to calibrate the developed MFD and p-MFD and evaluates the network performance under different proportions of dedicated bus lanes. The simulation results show that the larger the proportion of dedicated bus lanes, the greater the impact on the rising section and the stable section of MFD and the descending section and post-stable section of p-MFD. Further analysis for the sensitivity of simulation experiments found that the strategy of setting dedicated bus lanes will improve the efficiency of vehicle and passenger transport when the road network is in a smooth state and ensure the continuous output of passengers when the network is in a congested state.

Using GTFS to Calculate Travel Time Savings Potential of Bus Preferential Treatments

Dedicated bus lanes and other transit priority treatments are a cost-effective way to improve transit speed and reliability. However, creating a bus lane can be a contentious process; it requires justification to the public and frequently entails competition for federal grants. In addition, more complex bus networks are likely to have unknown locations where transit priority infrastructure would provide high value to riders. This analysis presents a methodology for estimating the value of bus preferential treatments for all segments of a given bus network. It calculates the passenger-weighted travel time savings potential for each inter-stop segment based on schedule padding. The input data, ridership data, and General Transit Feed Specification (GTFS) trip-stop data are universally accessible to transit agencies. This study examines the 2018 Metropolitan Atlanta Rapid Transit Authority (MARTA) bus network and identifies a portion of route 39 on Buford Highway as an example candidate for a bus lane corridor. The results are used to evaluate the value of time savings to passengers, operating cost savings to the agency, and other benefits that would result from implementing bus lanes on Buford Highway. This study does not extend to estimating the cost of transit priority infrastructure or recommending locations based on traffic flow characteristics. However, it does provide a reproducible methodology to estimate the value of transit priority treatments, and it identifies locations with high value, all using data that are readily available to transit agencies. Conducting this analysis provides a foundation for beginning the planning process for transit priority infrastructure.

Implementation Sequence Optimization for Dedicated Bus Lane Projects

Transportation projects are often implemented in phases, and the total project duration can span years. Optimization of the sequence in which transportation projects are implemented can decrease the severity of disruptions caused by construction, reduce the total cost of projects, and increase the present value of the benefits of the project. This paper presents a method for optimizing the sequence and location of dedicated bus lane implementations. The proposed method is based on a bi-level optimization framework. The lower level simulates the traffic using a link transmission model to evaluate car and bus delays, while the upper level optimizes the locations of bus lanes and/or the implementation sequence of a given bus lane configuration. If the budget or other resource constraints are binding, the optimized sequence uniquely determines the optimal schedule. The solution method is evaluated for a test network, and an analysis of sensitivity to different demand patterns and different bus lane configurations is conducted. First, the optimized locations for bus lane implementation are determined for an illustrative test network. Results suggest that, when considering the implementation sequence for the optimized set of bus lane locations, the sequence of implementation does not yield significant benefits. However, if bus lanes are implemented on every possible link, the results suggest that prioritizing the implementation of bus lanes on peripheral links would reduce the total cost the most. These locations coincide with the set of optimized locations for bus lane implementation. Further tests considering non-uniform demand patterns also confirm these findings.