Optimization of Rail Transit Alignments considering Vehicle Dynamics

2012 ◽  
Vol 2275 (1) ◽  
pp. 77-87 ◽  
Author(s):  
Xiaorong Lai ◽  
Paul Schonfeld
Keyword(s):  
Traffic Congestion ◽  
Vehicle Dynamics ◽  
Optimization Method ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Transit Systems ◽  
Track Geometry ◽  
User Costs ◽  
Trade Offs ◽  
The Impact

Urban rail transit systems are being extended throughout the world because of their large capacities, avoidance of traffic congestion, and environmental advantages. Various optimization models can help design rail transit alignments satisfying various track geometry constraints, but none of these models can account for the impacts of vehicle dynamics on operational and user costs. This paper presents a practical rail transit alignment optimization method for designing track alignments that accounts for vehicle dynamics. The method can generate alignments that improve the balance between the initial cost and the operation and user costs recurring throughout the system's life cycle. A heuristic based on a genetic algorithm is developed to search for solutions efficiently while interacting with the supporting geographic information system. A hypothetical topography scenario is created to illustrate the impact of vehicle dynamics on the trade-offs among system costs. The Baltimore, Maryland, Red Line is used as a case study to demonstrate that the model can find good solutions in regions with complex topographies.

scholarly journals Urban Rail Transit in Bangkok: Chronological Development Review and Impact on Residential Property Value

2021 ◽  
Vol 14 (1) ◽  
pp. 284
Author(s):  
Varameth Vichiensan ◽  
Vasinee Wasuntarasook ◽  
Yoshitsugu Hayashi ◽  
Masanobu Kii ◽  
Titipakorn Prakayaphun
Keyword(s):  
Urban Development ◽  
Traffic Congestion ◽  
Historical Review ◽  
Economic Loss ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Urban Structure ◽  
Hedonic Price ◽  
Property Value ◽  
The Impact

Bangkok suffered from the world’s worst traffic congestion in the 1990s due to rapidly increasing car ownership, reflecting the economic growth and road-dependent transport policy beginning in the 1960s. Due to its monocentric but scattered urban structure, traffic congestion is severe, causing tremendous economic loss, deteriorating air quality, and badly affecting the quality of life. A historical review reveals that the urban and transport plan and development were not efficiently coordinated, resulting in unorganized suburbanization and progressively more severe traffic congestion. It is important to reveal the impact of the transportation project on the housing market in order to incorporate the policies for transportation and urban development. To define the impact, the OLS hedonic price model and the local multiscale geographically weighted regression (MGWR) model were estimated, along with the condominium sales data. The results revealed that the impact of rail transit on a rise in property value significantly varied across the study area. It was estimated that, for the area along the major rail transit corridor in the city center, a premium of a location 100-m closer to the station would be more than 200 USD per square meter. At the same time, the value would be less than 80 USD for the area along the rail corridor in the suburb. These findings provide policy insights for future urban and railway development, including the proper coordination of rail transit development and urban development with subcenters, transit-oriented development, and improved pedestrian flow around transit stations.

scholarly journals Research on Decision Evaluation Model of HOV Lane Setting

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Ding Lv ◽  
Qunqi Wu ◽  
Bo Chen ◽  
Yahong Jiang
Keyword(s):  
Traffic Congestion ◽  
Road Traffic ◽  
Evaluation Model ◽  
Path Selection ◽  
Urban Traffic ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Travel Efficiency ◽  
Hov Lanes ◽  
The City

In order to achieve the purpose of improving the travel efficiency of commuters in the periphery of the city, expanding the beneficiary groups of urban rail transit, and alleviating urban road traffic congestion, when planning and setting up HOV in the periphery of the city, it is necessary to analyze the feasibility of HOV lane setting from both the demand conditions and the setting conditions. This paper combines machine learning to construct a decision-making evaluation model for HOV lane setting and studies the optimal layout model and algorithm of HOV lanes in service rail transit commuter chain. The setting, planning, and layout of HOV lanes are a two-way interactive process of traveler's path selection and designer's road planning. Finally, after the model is constructed, the performance of the system model is verified. The results show that the system studied in this paper can be used for traffic data and lane planning analysis. Therefore, in the process of urban operation, the HOV model constructed in this paper is mainly used to alleviate urban traffic and improve urban operation efficiency.

Effect of Urban Rail Transit Lines Linking the New Towns on Land Value Uplift in the Megacity Based on Difference-in-Difference Model

2020 ◽  
Vol 2674 (11) ◽  
pp. 317-328
Author(s):  
Junfang Li ◽  
Zhigang Liu ◽  
Jie Yu ◽  
Hua Hu
Keyword(s):  
Urban Rail Transit ◽  
Rail Transit ◽  
Land Value ◽  
Difference In Difference ◽  
Difference Model ◽  
Time Saving ◽  
New Towns ◽  
Business District ◽  
The Impact ◽  
Over Time

There already exist some rail transit lines linking the new towns to the center business district (LTC) in megacities. However, few lines between the new towns (LTT) exist. The paper examines whether, when, and how LTT cause land value uplift with LTC as the benchmark, which in turn can be used for feasibility analysis for value capture financing for the implementation of LTT. Evaluate the value in catchment and control area over time to confirm uplift. Difference-in-difference model (DID) is used to analyze when and how LTT raise the uplift. In the case study of Tokyo, DID estimators show the following homogenous results: firstly, the implicit land value (ILV) of LTT is all lower than LTC except that related to time saving to the center business district (CBD) in the announcement period, implying LTT are expected significantly to link to CBD then; secondly, ILV goes down over time sharply for LTT than LTC, implying the impact of LTT on the uplift is less sustainable than that of LTC; thirdly, sustainability of ILV as to time saving to the capital of the new town is more than that to CBD for LTT; lastly, ILV in the announcement period presents significantly distance-decay performance for both lines. Heterogeneity among the stations is detected for both lines; for LTT, the impact of proximity to the huge interchange station on land value uplift is slight. These results provide an evidence base for policy-makers to quantify the potential to raise financial funding for LTT.

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