scholarly journals Optimization of Transit Scheduling Combined with Short-Turn Service Based on Real-Time Passenger Flow

2020 ◽  
Author(s):  
Ailing Huang ◽  
Yijing Miao ◽  
Jiarui Li
Keyword(s):  
Real Time ◽  
Public Transit ◽  
Load Ratio ◽  
Total System ◽  
Transit System ◽  
Fleet Size ◽  
Passenger Flow ◽  
Vehicle Data ◽  
Transit Scheduling ◽  
Working Day

In view of a series of problems, such as unable to meet the needs of passengers, high full load ratio or waste of carrying capacity on unbalanced passenger flow sections caused by the all-stop fleet scheduling in the urban public transit system, this paper proposed a bus combination scheduling strategy with considering short-turn service based on the imbalance coefficient of passenger flow and a method to determine the turning back point. A combined dispatching optimization model is established with the objective function of minimizing the total system cost which includes the waiting time cost of passengers, the congestion feeling cost and the operation cost of public transit enterprises. The headways of short-turn and all-stop scheme are optimized by the combined scheduling model, and the solution method is proposed. Taking Beijing No. A bus line as an empirical analysis object, the real-time passenger flow and vehicle data in a working day are collected and analyzed, and the optimized scheme of short-turn service combination scheduling is obtained. The results show that compared with the traditional all-stop fleet scheduling, the optimized short-turn service combination scheduling can reduce the fleet size by 4.9% and effectively improve the operation efficiency and system benefits.

scholarly journals Three Revised Kalman Filtering Models for Short-Term Rail Transit Passenger Flow Prediction

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Pengpeng Jiao ◽  
Ruimin Li ◽  
Tuo Sun ◽  
Zenghao Hou ◽  
Amir Ibrahim
Keyword(s):  
Real Time ◽  
Nonparametric Regression ◽  
Kalman Filtering ◽  
Rail Transit ◽  
Correction Coefficient ◽  
Short Term ◽  
Transit System ◽  
Filtering Method ◽  
Passenger Flow ◽  
Model Based

Short-term prediction of passenger flow is very important for the operation and management of a rail transit system. Based on the traditional Kalman filtering method, this paper puts forward three revised models for real-time passenger flow forecasting. First, the paper introduces the historical prediction error into the measurement equation and formulates a revised Kalman filtering model based on error correction coefficient (KF-ECC). Second, this paper employs the deviation between real-time passenger flow and corresponding historical data as state variable and presents a revised Kalman filtering model based on Historical Deviation (KF-HD). Third, the paper integrates nonparametric regression forecast into the traditional Kalman filtering method using a Bayesian combined technique and puts forward a revised Kalman filtering model based on Bayesian combination and nonparametric regression (KF-BCNR). A case study is implemented using statistical passenger flow data of rail transit line 13 in Beijing during a one-month period. The reported prediction results show that KF-ECC improves the applicability to historical trend, KF-HD achieves excellent accuracy and stability, and KF-BCNR yields the best performances. Comparisons among different periods further indicate that results during peak periods outperform those during nonpeak periods. All three revised models are accurate and stable enough for on-line predictions, especially during the peak periods.

Using Deficit Function to Determine the Minimum Fleet Size of an Autonomous Modular Public Transit System

2020 ◽  
Vol 2674 (11) ◽  
pp. 532-541 ◽  
Author(s):  
Tao Liu ◽  
Avishai (Avi) Ceder ◽  
Andreas Rau
Keyword(s):  
Autonomous Vehicles ◽  
Large Scale ◽  
Public Transit ◽  
Programming Model ◽  
Real Life ◽  
Single Line ◽  
Continuum Approximation ◽  
Transit System ◽  
Fleet Size ◽  
Minimum Number

Emerging technologies, such as connected and autonomous vehicles, electric vehicles, and information and communication, are surrounding us at an ever-increasing pace, which, together with the concept of shared mobility, have great potential to transform existing public transit (PT) systems into far more user-oriented, system-optimal, smart, and sustainable new PT systems with increased service connectivity, synchronization, and better, more satisfactory user experiences. This work analyses such a new PT system comprised of autonomous modular PT (AMPT) vehicles. In this analysis, one of the most challenging tasks is to accurately estimate the minimum number of vehicle modules, that is, its minimum fleet size (MFS), required to perform a set of scheduled services. The solution of the MFS problem of a single-line AMPT system is based on a graphical method, adapted from the deficit function (DF) theory. The traditional DF model has been extended to accommodate the definitions of an AMPT system. Some numerical examples are provided to illustrate the mathematical formulations. The limitations of traditional continuum approximation models and the equivalence between the extended DF model and an integer programming model are also provided. The extended DF model was applied, as a case study, to a single line of an AMPT system, the dynamic autonomous road transit (DART) system in Singapore. The results show that the extended DF model is effective in solving the MFS problem and has the potential to be applied to solving real-life MFS problems of large-scale, multi-line and multi-terminal AMPT systems.

scholarly journals OPTIMIZING LIMITED-STOP BUS SERVICES ALONG A PUBLIC TRANSIT CORRIDOR WITH A DIFFERENTIAL FARE STRUCTURE

Transport ◽  
2019 ◽  
Vol 34 (4) ◽  
pp. 476-489 ◽  
Author(s):  
Chunyan Tang ◽  
Ying-En Ge ◽  
William H. K. Lam
Keyword(s):  
Social Cost ◽  
Travel Demand ◽  
Public Transit ◽  
Social Equity ◽  
Heavy Load ◽  
Transit System ◽  
Fleet Size ◽  
Bus Service ◽  
Total Social Cost ◽  
Bus Services

Limited-stop bus services are a highly efficient way to release more potential of the public transit system to meet travel demand, especially under constraints on vehicle fleet size and transportation infrastructure. This work first proposes a visualized fare table for the design of limited-stop bus services along a public transit corridor, along which many lines of public transit carry a heavy load of demand back and forth every working day. Based on this proposed fare table, a set of fare strategies and desired aims of fare policy, a differentiated fare structure is established to improve social equity and increase revenue. The nature of the structure can help travellers understand how to be charged between their origins and destinations (e.g. flat, time-based, stop-based or quality-based pricing) and then plan their trips efficiently. Secondly, a model is formulated to minimize the total social cost in designing a fixed demand limited-stop bus service system with a differentiated fare structure. Thirdly, numerical results are carried out with sensitivity analysis within three scenarios of differentiated fare structures. It is found that a differentiated fare structure has a great effect on passenger path choice behaviour and resulting optimal design of bus services. An attractive feature of this differentiated fare structure is that it could not only enhance the operator’s revenue and social equity but also reduce passenger transfers and social cost.

Adaptive route choice model for public transit systems: an application of Markov decision processes

2012 ◽  
Vol 39 (8) ◽  
pp. 915-924 ◽  
Author(s):  
Behzad Rouhieh ◽  
Ciprian Alecsandru
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Public Transit ◽  
Choice Model ◽  
Transportation Systems ◽  
Transit System ◽  
Transit Systems ◽  
The Impact

Over the past couple of decades the advancements in the areas of information and computational technology allowed for a variety of intelligent transportation systems developments and deployments. This study investigates an advanced traveler information system (ATIS) and (or) an advanced public transit system (APTS) adaptive and real-time transit routing component. The proposed methodology is applied to bus routes with fixed, predefined bus line alignments. It is shown that routing buses on such systems can be modeled in real-time by employing an associated Markov chain with reward model to minimize the impact of congested traffic conditions on the travelers and the overall operation cost of the transit system. A case study using a traffic and transit data from a real-world bus line was used to apply the proposed bus routing approach. It was found that under certain traffic congestion conditions buses should be re-routed to minimize their travel time and the associated system costs. The hypothetical congestion scenarios investigated show that individual bus travel time delays range between 50 and 740 s when the proposed adaptive routing is employed. The proposed methodology is also suitable for application to transit systems that run on a demand-adaptive basis (the bus line alignment changes with the travelers demand). Additional calibration and future integration of the system into specific ATIS and (or) APTS user services will be investigated.

scholarly journals Understanding Users’ Satisfaction Towards Public Transit System in India: A Case-Study of Mumbai

2021 ◽  
Vol 10 (3) ◽  
pp. 155
Author(s):  
Rahul Das
Keyword(s):  
Service Quality ◽  
Public Transport ◽  
Travel Demand ◽  
Resource Constraints ◽  
Public Transit ◽  
Sampling Bias ◽  
Relevant Information ◽  
Transit System ◽  
Transport Services ◽  
Developing Cities

In this work, we present a novel approach to understand the quality of public transit system in resource constrained regions using user-generated contents. With growing urban population, it is getting difficult to manage travel demand in an effective way. This problem is more prevalent in developing cities due to lack of budget and proper surveillance system. Due to resource constraints, developing cities have limited infrastructure to monitor transport services. To improve the quality and patronage of public transit system, authorities often use manual travel surveys. But manual surveys often suffer from quality issues. For example, respondents may not provide all the detailed travel information in a manual travel survey. The survey may have sampling bias. Due to close-ended design (specific questions in the questionnaire), lots of relevant information may not be captured in a manual survey process. To address these issues, we investigated if user-generated contents, for example, Twitter data, can be used to understand service quality in Greater Mumbai in India, which can complement existing manual survey process. To do this, we assumed that, if a tweet is relevant to public transport system and contains negative sentiment, then that tweet expresses user’s dissatisfaction towards the public transport service. Since most of the tweets do not have any explicit geolocation, we also presented a model that does not only extract users’ dissatisfaction towards public transit system but also retrieves the spatial context of dissatisfaction and the potential causes that affect the service quality. It is observed that a Random Forest-based model outperforms other machine learning models, while yielding 0.97 precision and 0.88 F1-score.

scholarly journals An LSTM-Based Method Considering History and Real-Time Data for Passenger Flow Prediction

2020 ◽  
Vol 10 (11) ◽  
pp. 3788 ◽  
Author(s):  
Qi Ouyang ◽  
Yongbo Lv ◽  
Jihui Ma ◽  
Jing Li
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Short Term Memory ◽  
Historical Data ◽  
Time Interval ◽  
Information Coding ◽  
Time Data ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Real Time Data

With the development of big data and deep learning, bus passenger flow prediction considering real-time data becomes possible. Real-time traffic flow prediction helps to grasp real-time passenger flow dynamics, provide early warning for a sudden passenger flow and data support for real-time bus plan changes, and improve the stability of urban transportation systems. To solve the problem of passenger flow prediction considering real-time data, this paper proposes a novel passenger flow prediction network model based on long short-term memory (LSTM) networks. The model includes four parts: feature extraction based on Xgboost model, information coding based on historical data, information coding based on real-time data, and decoding based on a multi-layer neural network. In the feature extraction part, the data dimension is increased by fusing bus data and points of interest to improve the number of parameters and model accuracy. In the historical information coding part, we use the date as the index in the LSTM structure to encode historical data and provide relevant information for prediction; in the real-time data coding part, the daily half-hour time interval is used as the index to encode real-time data and provide real-time prediction information; in the decoding part, the passenger flow data for the next two 30 min interval outputs by decoding all the information. To our best knowledge, it is the first time to real-time information has been taken into consideration in passenger flow prediction based on LSTM. The proposed model can achieve better accuracy compared to the LSTM and other baseline methods.

scholarly journals An Approach for Real-time Estimation of Railway Passenger Flow

2010 ◽  
Vol 51 (2) ◽  
pp. 82-88 ◽  
Author(s):  
Yoichi SUGIYAMA ◽  
Hiroshi MATSUBARA ◽  
Shuichi MYOJO ◽  
Kazuki TAMURA ◽  
Naoya OZAKI
Keyword(s):  
Real Time ◽  
Time Estimation ◽  
Passenger Flow ◽  
Real Time Estimation ◽  
Railway Passenger
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