scholarly journals Driving Behavior Analysis of City Buses Based on Real-Time GNSS Traces and Road Information

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 687 ◽  
Author(s):  
Yuan Yang ◽  
Jingjie Yan ◽  
Jing Guo ◽  
Yujin Kuang ◽  
Mingyang Yin ◽  
...  
Keyword(s):  
Behavior Analysis ◽  
Real Time ◽  
Public Transportation ◽  
Driver Behavior ◽  
Field Tests ◽  
Satellite System ◽  
Driving Behavior ◽  
Urban Environments ◽  
Urban Traffic ◽  
Intelligent Transport System

The driving behavior of bus drivers is related to the safety of all passengers and regulation of urban traffic. In order to analyze the relevant characteristics of speed and acceleration, accurate bus trajectories and patterns are essential for driver behavior analysis and development of effective intelligent public transportation. Exploiting real-time vehicle tracking, this paper develops a platform with vehicle-mounted terminals using differential global navigation satellite system (DGNSS) modules for driver behavior analysis. The DGNSS traces were used to derive the vehicle trajectories, which were then linked to road information to produce speed and acceleration matrices. Comprehensive field tests were undertaken on multiple bus routes in urban environments. The spatiotemporal results indicate that the platform can automatically and accurately extract the driving behavior characteristics. Furthermore, the platform’s visual function can be used to effectively monitor driving risks, such as speeding and fierce acceleration, in multiple bus routes. The details of the platform’s features are provided for intelligent transport system (ITS) design and applications.

Low-Cost Receiver and Network Real-Time Kinematic Positioning for use in Connected and Autonomous Vehicles

2019 ◽  
Vol 72 (04) ◽  
pp. 917-930
Author(s):  
Fang-Shii Ning ◽  
Xiaolin Meng ◽  
Yi-Ting Wang
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
High Performance ◽  
Low Cost ◽  
Satellite System ◽  
High Accuracy ◽  
Future Trend ◽  
Intelligent Transport System ◽  
Positioning System ◽  
Gnss Receiver

Connected and Autonomous Vehicles (CAVs) have been researched extensively for solving traffic issues and for realising the concept of an intelligent transport system. A well-developed positioning system is critical for CAVs to achieve these aims. The system should provide high accuracy, mobility, continuity, flexibility and scalability. However, high-performance equipment is too expensive for the commercial use of CAVs; therefore, the use of a low-cost Global Navigation Satellite System (GNSS) receiver to achieve real-time, high-accuracy and ubiquitous positioning performance will be a future trend. This research used RTKLIB software to develop a low-cost GNSS receiver positioning system and assessed the developed positioning system according to the requirements of CAV applications. Kinematic tests were conducted to evaluate the positioning performance of the low-cost receiver in a CAV driving environment based on the accuracy requirements of CAVs. The results showed that the low-cost receiver satisfied the “Where in Lane” accuracy level (0·5 m) and achieved a similar positioning performance in rural, interurban, urban and motorway areas.

scholarly journals Data-Driven Real-Time Online Taxi-Hailing Demand Forecasting Based on Machine Learning Method

2020 ◽  
Vol 10 (19) ◽  
pp. 6681 ◽  
Author(s):  
Zhizhen Liu ◽  
Hong Chen ◽  
Xiaoke Sun ◽  
Hengrui Chen
Keyword(s):  
Real Time ◽  
Public Transportation ◽  
Demand Forecasting ◽  
Gradient Boosting ◽  
Intelligent Transport System ◽  
Transportation Services ◽  
Intelligent Transport ◽  
Time Prediction ◽  
Extreme Gradient Boosting ◽  
Demand Forecasting Model

The development of the intelligent transport system has created conditions for solving the supply–demand imbalance of public transportation services. For example, forecasting the demand for online taxi-hailing could help to rebalance the resource of taxis. In this research, we introduced a method to forecast real-time online taxi-hailing demand. First, we analyze the relation between taxi demand and online taxi-hailing demand. Next, we propose six models containing different information based on backpropagation neural network (BPNN) and extreme gradient boosting (XGB) to forecast online taxi-hailing demand. Finally, we present a real-time online taxi-hailing demand forecasting model considering the projected taxi demand (“PTX”). The results indicate that including more information leads to better prediction performance, and the results show that including the information of projected taxi demand leads to a reduction of MAPE from 0.190 to 0.183 and an RMSE reduction from 23.921 to 21.050, and it increases R2 from 0.845 to 0.853. The analysis indicates the demand regularity of online taxi-hailing and taxi, and the experiment realizes real-time prediction of online taxi-hailing by considering the projected taxi demand. The proposed method can help to schedule online taxi-hailing resources in advance.

CITRAM - Citizen Science for Traffic Management

2020 ◽  
Author(s):  
Benedikt Gräler ◽  
Christoph Doll ◽  
Jürgen Mück ◽  
Albert Remke ◽  
Diana Schramm ◽  
...  
Keyword(s):  
Real Time ◽  
Citizen Science ◽  
Traffic Management ◽  
Combustion Engine ◽  
Open Data ◽  
Field Tests ◽  
Driving Behavior ◽  
Traffic Light ◽  
Floating Car Data ◽  
German Cities

<p>The CITRAM project aims at improving traffic quality in cities with the help of floating car data provided by citizens. During CITRAM, the citizen science platform enviroCar (https://www.enviroCar.org) has been extended and is used to collect floating car data in three German cities. Citizens are invited to collect data in designated field tests while driving their day-to-day routes. These collected trajectories are anonymised, stored and published under an open data policy in a central server.</p><p>Dedicated postprocessing services using new concepts for evaluation and visualization analyze the data on a daily basis deriving traffic quality characteristics. The raw data and the processed reports are used by the cities and their planners to assess the traffic quality and to deduce actions to improve traffic management.</p><p>The project also raises the awareness of an environmentally improved driving behavior through the collection of floating car data enriched with individual energy and fuel consumption along the recorded routes of electric and internal combustion engine driven cars. Through the integration of municipal information infrastructure into a dedicated real-time Smart City platform and a model accounting for the dynamic control of traffic light systems, a traffic light phase assistant app (ECOMAT) further supports the driver in a foresighted and energy optimized driving behavior by providing Green Light Optimised Speed Advisory (GLOSA) and Time To Green (TTG) information in real-time.</p><p>The motivation of CITRAM is the coupling of system components that enable scientists, traffic engineers and citizens to collaborate on knowledge acquisition concerning driving in motorized traffic. We will present the developed tool set and the results from the analysis of floating car data collected by citizens. The analysis assess the quality of traffic flow within the municipality as well as characteristics of individual trajectories or dedicated routes.</p>

scholarly journals ANALYSIS OF THE FLOATING CAR DATA OF TURIN PUBLIC TRANSPORTATION SYSTEM: FIRST RESULTS

2018 ◽  
Vol XLII-4 ◽  
pp. 515-521
Author(s):  
R. Ravanelli ◽  
M. Crespi
Keyword(s):  
Public Transportation ◽  
Low Cost ◽  
Temporal Analysis ◽  
Satellite System ◽  
Transportation System ◽  
Urban Environments ◽  
Floating Car Data ◽  
Public Transportation System ◽  
Spatio Temporal ◽  
Global Navigation Satellite

<p><strong>Abstract.</strong> Global Navigation Satellite System (GNSS) sensors represent nowadays a mature technology, low-cost and efficient, to collect large spatio-temporal datasets (Geo Big Data) of vehicle movements in urban environments. Anyway, to extract the mobility information from such Floating Car Data (FCD), specific analysis methodologies are required. In this work, the first attempts to analyse the FCD of the Turin Public Transportation system are presented. Specifically, a preliminary methodology was implemented, in view of an automatic and possible real-time impedance map generation. The FCD acquired by all the vehicles of the Gruppo Torinese Trasporti (GTT) company in the month of April 2017 were thus processed to compute their velocities and a visualization approach based on Osmnx library was adopted. Furthermore, a preliminary temporal analysis was carried out, showing higher velocities in weekend days and not peak hours, as could be expected. Finally, a method to assign the velocities to the line network topology was developed and some tests carried out.</p>

scholarly journals Deep Wide Spatial-Temporal Based Transformer Networks Modeling for the Next Destination According to the Taxi Driver Behavior Prediction

2020 ◽  
Vol 11 (1) ◽  
pp. 17
Author(s):  
Zain Ul Abideen ◽  
Heli Sun ◽  
Zhou Yang ◽  
Rana Zeeshan Ahmad ◽  
Adnan Iftekhar ◽  
...  
Keyword(s):  
Waiting Time ◽  
Traffic Congestion ◽  
Human Mobility ◽  
Driver Behavior ◽  
Urban Traffic ◽  
Urban Transport ◽  
Geographical Information ◽  
Taxi Driver ◽  
Intelligent Transport System ◽  
Neural Network Approach

This paper uses a neural network approach transformer of taxi driver behavior to predict the next destination with geographical factors. The problem of predicting the next destination is a well-studied application of human mobility, for reducing traffic congestion and optimizing the electronic dispatching system’s performance. According to the Intelligent Transport System (ITS), this kind of task is usually modeled as a multi-class problem. We propose the novel model Deep Wide Spatial-Temporal-Based Transformer Networks (DWSTTNs). In our approach, the encoder and decoder are the transformer’s primary units; with the help of Location-Based Social Networks (LBSN), we encode the geographical information based on visited semantic locations. In particular, we trained our model for the exact longitude and latitude coordinates to predict the next destination. The benefit in the real world of this kind of research is to reduce the customer waiting time for a ride and driver waiting time to pick up a customer. Taxi companies can also optimize their management to improve their company’s service, while urban transport planner can use this information to better plan the urban traffic. We conducted extensive experiments on two real-word datasets, Porto and Manhattan, and the performance was improved compared to the previous models.

PENGEMBANGAN ”REAL TIME TRAFFIC INFORMATION SYSTEM” BAGI PENGGUNA JALAN

2013 ◽  
Vol 12 (3) ◽  
Author(s):  
Rusmadi Suyuti
Keyword(s):  
Information System ◽  
Real Time ◽  
Travel Demand ◽  
Urban Traffic ◽  
Road User ◽  
Traffic Information ◽  
Estimation Methods ◽  
Traffic Counts ◽  
Real Time Traffic ◽  
Demand Models

Traffic information condition is a very useful  information for road user because road user can choose his best route for each trip from his origin to his destination. The final goal for this research is to develop real time traffic information system for road user using real time traffic volume. Main input for developing real time traffic information system is an origin-destination (O-D) matrix to represent the travel pattern. However, O-D matrices obtained through a large scale survey such as home or road side interviews, tend to be costly, labour intensive and time disruptive to trip makers. Therefore, the alternative of using traffic counts to estimate O-D matrices is particularly attractive. Models of transport demand have been used for many years to synthesize O-D matrices in study areas. A typical example of the approach is the gravity model; its functional form, plus the appropriate values for the parameters involved, is employed to produce acceptable matrices representing trip making behaviour for many trip purposes and time periods. The work reported in this paper has combined the advantages of acceptable travel demand models with the low cost and availability of traffic counts. Two types of demand models have been used: gravity (GR) and gravity-opportunity (GO) models. Four estimation methods have been analysed and tested to calibrate the transport demand models from traffic counts, namely: Non-Linear-Least-Squares (NLLS), Maximum-Likelihood (ML), Maximum-Entropy (ME) and Bayes-Inference (BI). The Bandung’s Urban Traffic Movement survey has been used to test the developed method. Based on several statistical tests, the estimation methods are found to perform satisfactorily since each calibrated model reproduced the observed matrix fairly closely. The tests were carried out using two assignment techniques, all-or-nothing and equilibrium assignment.  

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