Research
Research
This project focuses on improving the reliability of transit line operations, particularly in systems employing short-turning strategies. Short turning allows for higher service frequency in high-demand sections but introduces operational complexities. To address these challenges, a simulation engine was developed to test various control strategies, such as holding and load balancing, to enhance service reliability and optimize resource utilization.
The primary objectives of this project are to evaluate the effectiveness of real-time control strategies, such as holding and headway adjustments, in improving service reliability in the presence of short-turning trains. Additionally, the project aims to explore strategies like load balancing to better utilize train capacity, reduce denied boardings, and minimize overcrowding, particularly at key stations.
Simulation-based analysis demonstrated that control strategies, such as holding trains to balance headways, effectively reduce wait times and denied boardings. Load balancing strategies, which adjust headways to improve capacity utilization, showed potential in reducing overcrowding and optimizing passenger distribution. However, uniform headway control alone was found insufficient to address uneven passenger loads, highlighting the need for more dynamic dispatching decisions at terminals and key stations.
Driving cycles are a set of driving conditions and are crucial for the existing emission estimation model to evaluate vehicle performance, fuel efficiency, and emissions, by matching them with average speed to calculate the operating modes, such as braking, idling, and cruising.
While existing emission estimation models are powerful tools, their reliance on predefined driving cycles can be limiting, as these cycles often do not accurately represent regional driving conditions, making the models less effective for city-wide analyses.
To solve this problem, this research proposes a neural network-based framework to estimate operating mode distributions bypassing the driving cycle development phase, utilizing macroscopic variables such as speed, flow, and link infrastructure attributes.
The proposed method is validated using a well-calibrated microsimulation model of Brookline MA, the United States. The results indicate that the proposed framework outperforms the operating mode distribution calculated by MOVES based on default driving cycles, providing a closer match to the actual operating mode distribution derived from trajectory data. The proposed model can be utilized for real-time emissions monitoring by providing rapid and accurate emissions estimates with easily accessible inputs.
As the docked bike-sharing system (DBS) differs widely from FFBS and in docked bike-sharing, bikes are parked only at the docking stations. During the repositioning operation, both types of bikes, usable and faulty, are supposed to be at fixed capacity docking stations.
The use of FFBS has increased swiftly worldwide and many people are benefiting from it. However, there is always a deficiency or excess of sharing bikes in the system due to uneven flows that are either unable to accommodate users at some points or remain sedentary at some other points. Therefore, periodical repositioning of bikes in the system is inevitable to fulfill the commuters' demand at all service locations.
The main contribution of the study conducted by me lies to present a framework for static FFBS repositioning with faulty bikes, where the framed model help bike-sharing companies: i) to perform usable bikes repositioning at different nodes, ii) in gathering faulty bikes from different locations to the depots, and iii) in the distribution of revamped bikes into the system.
My second project was to formulate and design a model that focuses on the use of BEVs instead of using conventional ICEVs deployed for the rebalancing an FFBS.
In today’s world, the health of economic growth is closely linked to the transport system. However, internal combustion engine vehicles (ICEVs) mainly power the current transport systems. This not only makes the world dependent on the global oil market but also generates the most important source of greenhouse gas (GHG) emissions. Due to the expected shortage of oil and increased emission of toxic gases, more and more talent and resources are being built up to meet the challenges of reducing oil dependence, climate change, and sustainable transport systems.
My developed model formulation solves the problem in various situations. The results show that the vehicle battery capacity has a significant effect on the routing plan. A vehicle with small battery capacity visits the charging station after a short trip. The extra visits to the charging station not only increase operation cost but also increase the operation time. In addition, excessive battery charging events further delay the operation process. When the battery capacity is increased from 16 kWh to 60 kWh, the total traveling cost reduces up to 17.8% and 20.4% with one and two operating vehicles, respectively. Likewise, the cost of a vehicle with a capacity of 30 bikes is 44% lesser than the cost of the vehicle with a capacity of 15 bikes. Since the BEVs have a high initial cost. Therefore, a detailed cost analysis is required for various capacity vehicles.
Traffic state estimation (TSE) refers to the prediction of traffic state variables such as flow, density, and speed of road segments using partially observed traffic data.
The traffic conditions on road segments in a network are usually described by macroscopic traffic state variables, such as flow rate, vehicle speed, and vehicle density as traffic streams. Transport planners identify congestion levels and traffic demands, as well as bottlenecks on roadways, through these indicators. However, these important measurements are not available for all locations or times due to physical and budget constraints of the measurements; even if the availability issue is not a concern; undesired noises in the measurements have been making ITS traffic management operations difficult. Combining factors such as the cost of sensor installation, the accuracy of vehicle detection techniques, and restrictions on data storage and transmission can often lead to partial observations of traffic state variables. To deliver effective traffic management, it is vital to estimate traffic state variables at locations without sensor data.
The experimental results show that the presented framework accurately learns the complex traffic dynamics over a circular traffic network. The predicted speeds match the actual speeds at almost all the locations other than the intersection points. The error at the intersection points is due to discrepancies in the actual data. Theoretically, the flow conservation condition must be satisfied at the intersection points. However, the actual simulated data violate the flow conservation, and this violation is potentially the result of considering average speeds over small cells.