Energy-efficient receding horizon trajectory planning of high-speed trains using real-time traffic information

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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Pontryagin’s minimum principle based fuzzy adaptive energy management for hybrid electric vehicle using real-time traffic information

Applied Energy ◽

10.1016/j.apenergy.2021.116467 ◽

2021 ◽

Vol 286 ◽

pp. 116467

Author(s):

Dehua Shi ◽

Sheng Liu ◽

Yingfeng Cai ◽

Shaohua Wang ◽

Haoran Li ◽

...

Keyword(s):

Real Time ◽

Electric Vehicle ◽

Energy Management ◽

Hybrid Electric Vehicle ◽

Minimum Principle ◽

Traffic Information ◽

Real Time Traffic ◽

Pontryagin's Minimum Principle ◽

Hybrid Electric ◽

Fuzzy Adaptive

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A real-time control method-based simulation for high-speed trains on large-scale rail network

International Journal of Modern Physics C ◽

10.1142/s0129183117501261 ◽

2017 ◽

Vol 28 (10) ◽

pp. 1750126 ◽

Cited By ~ 1

Author(s):

Yutong Liu ◽

Chengxuan Cao ◽

Yaling Zhou ◽

Ziyan Feng

Keyword(s):

Real Time ◽

Traffic Flow ◽

High Speed ◽

Large Scale ◽

Simulation Method ◽

Real Time Control ◽

Rail Network ◽

Time Control ◽

High Speed Trains ◽

Rail Traffic

In this paper, an improved real-time control model based on the discrete-time method is constructed to control and simulate the movement of high-speed trains on large-scale rail network. The constraints of acceleration and deceleration are introduced in this model, and a more reasonable definition of the minimal headway is also presented. Considering the complicated rail traffic environment in practice, we propose a set of sound operational strategies to excellently control traffic flow on rail network under various conditions. Several simulation experiments with different parameter combinations are conducted to verify the effectiveness of the control simulation method. The experimental results are similar to realistic environment and some characteristics of rail traffic flow are also investigated, especially the impact of stochastic disturbances and the minimal headway on the rail traffic flow on large-scale rail network, which can better assist dispatchers in analysis and decision-making. Meanwhile, experimental results also demonstrate that the proposed control simulation method can be in real-time control of traffic flow for high-speed trains not only on the simple rail line, but also on the complicated large-scale network such as China’s high-speed rail network and serve as a tool of simulating the traffic flow on large-scale rail network to study the characteristics of rail traffic flow.

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The Research of Hierarchical Data Fusion Based on Three-tiers Wireless Sensor Networks for Urban Real Time Traffic Information Monitoring

2010 Fourth International Conference on Genetic and Evolutionary Computing ◽

10.1109/icgec.2010.203 ◽

2010 ◽

Author(s):

Huaping Yu

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Data Fusion ◽

Real Time ◽

Wireless Sensor ◽

Traffic Information ◽

Hierarchical Data ◽

Real Time Traffic ◽

Information Monitoring

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Mobile Road Traffic Management System Using Weighted Sensor

International Journal of Interactive Mobile Technologies (iJIM) ◽

10.3991/ijim.v11i5.6745 ◽

2017 ◽

Vol 11 (5) ◽

pp. 147 ◽

Cited By ~ 2

Author(s):

Solomon Adegbenro Akinboro ◽

Johnson A Adeyiga ◽

Adebayo Omotosho ◽

Akinwale O Akinwumi

Keyword(s):

Real Time ◽

Traffic Control ◽

Urban Areas ◽

Traffic Management ◽

Management System ◽

Road Traffic ◽

Traffic Information ◽

Traffic Light ◽

Real Time Traffic ◽

Traffic Management System

Vehicular traffic is continuously increasing around the world, especially in urban areas, and the resulting congestion has become a major concern to automobile users. The popular static electric traffic light controlling system can no longer sufficiently manage the traffic volume in large cities where real time traffic control is paramount to deciding best route. The proposed mobile traffic management system provides users with traffic information on congested roads using weighted sensors. A prototype of the system was implemented using Java SE Development Kit 8 and Google map. The model was simulated and the performance was assessed using response time, delay and throughput. Results showed that, mobile devices are capable of assisting road users’ in faster decision making by providing real-time traffic information and recommending alternative routes.

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RealTE: Real-time Trajectory Estimation Over Large-Scale Road Networks

Journal of Web Engineering ◽

10.13052/jwe1540-9589.21210 ◽

2022 ◽

Author(s):

Qibin Zhou ◽

Qingang Su ◽

Dingyu Yang

Keyword(s):

Travel Time ◽

Real Time ◽

High Speed ◽

Road Network ◽

Large Scale ◽

Road Networks ◽

Trajectory Data ◽

Real Time Traffic ◽

Histogram Estimation ◽

Traffic Estimation

Real-time traffic estimation focuses on predicting the travel time of one travel path, which is capable of helping drivers selecting an appropriate or favor path. Statistical analysis or neural network approaches have been explored to predict the travel time on a massive volume of traffic data. These methods need to be updated when the traffic varies frequently, which incurs tremendous overhead. We build a system RealTER⁢e⁢a⁢l⁢T⁢E, implemented on a popular and open source streaming system StormS⁢t⁢o⁢r⁢m to quickly deal with high speed trajectory data. In RealTER⁢e⁢a⁢l⁢T⁢E, we propose a locality-sensitive partition and deployment algorithm for a large road network. A histogram estimation approach is adopted to predict the traffic. This approach is general and able to be incremental updated in parallel. Extensive experiments are conducted on six real road networks and the results illustrate RealTE achieves higher throughput and lower prediction error than existing methods. The runtime of a traffic estimation is less than 11 seconds over a large road network and it takes only 619619 microseconds for model updates.

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Sistem Pengamatan Kondisi Lalu Lintas Berbasis Data GPS pada Smartphone (Studi Kasus : Kota Denpasar)

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2020741909 ◽

2020 ◽

Vol 7 (4) ◽

pp. 667

Author(s):

Gede Herdian Setiawan ◽

I Ketut Dedy Suryawan

Keyword(s):

Real Time ◽

Traffic Congestion ◽

Data Retrieval ◽

Traffic Information ◽

Vehicle Speed ◽

Observation Model ◽

Economic Activities ◽

Traffic Condition ◽

Traffic Conditions ◽

Real Time Traffic

Pertumbuhan jumlah kendaraan yang semakin meningkat setiap tahunnya mengakibatkan volume kendaraan yang melintasi ruas jalan semakin padat yang kerap mengakibatkan kemacetan lalu lintas. Kemacetan lalu lintas dapat menjadi beban biaya yang signifikan terhadap kegiatan ekonomi masyarakat. Informasi lalu lintas yang dinamis seperti informasi kondisi lalu lintas secara langsung (real time) akan membantu mempengaruhi aktivitas masyarakat pengguna lalu lintas untuk melakukan perencanaan dan penjadwalan aktivitas yang lebih baik. Penelitian ini mengusulkan model pengamatan kondisi lalu lintas berbasis data GPS pada smartphone, untuk informasi kondisi lalu lintas secara langsung. GPS Receiver pada smartphone menghasilkan data lokasi secara instan dan bersifat mobile sehingga dapat digunakan untuk pengambilan data kecepatan kendaraan secara langsung. Kecepatan kendaraan diperoleh berdasarkan jarak perpindahan koordinat kendaraan dalam satuan detik selanjutnya di konversi menjadi satuan kecepatan (km/jam) kemudian data kecepatan kendaraan di proses menjadi informasi kondisi lalu lintas. Secara menyeluruh model pengamatan berfokus pada tiga tahapan, yaitu akuisisi data kecepatan kendaraan berbasis GPS pada smartphone, pengiriman data kecepatan dan visualisasi kondisi lalu lintas berbasis GIS. Pengujian dilakukan pada ruas jalan kota Denpasar telah mampu mendapatkan data kecepatan kendaraan dan mampu menunjukkan kondisi lalu lintas secara langsung dengan empat kategori keadaan lalu lintas yaitu garis berwarna hitam menunjukkan lalu lintas macet dengan kecepatan kendaraan kurang dari 17 km/jam, merah menunjukkan padat dengan kecepatan kendaraan 17 km/jam sampai 27 km/jam, kuning menunjukkan sedang dengan kecepatan kendaraan 26 km/jam sampai 40 km/jam dan hijau menunjukkan lancar dengan kecepatan kendaraan diatas 40 km/jam. AbstractThe growth in the number of vehicles that is increasing every year has resulted in the volume of vehicles crossing the road increasingly congested which often results in traffic congestion. Traffic congestion can be a significant cost burden on economic activities. Dynamic traffic information such as information on real time traffic conditions will help influence the activities of the traffic user community to better plan and schedule activities. This study proposes a traffic condition observation model based on GPS data on smartphones, for information on real time traffic conditions. The GPS Receiver on the smartphone produces location and coordinate data instantly and is mobile so that it can be used for direct vehicle speed data retrieval. Vehicle speed is obtained based on the displacement distance of the vehicle's coordinates in units of seconds and then converted into units of speed (km / h), the vehicle speed data is then processed into information on traffic conditions. Overall, the observation model focuses on three stages, namely GPS-based vehicle speed data acquisition on smartphones, speed data delivery and visualization of GIS-based traffic conditions. Tests carried out on the Denpasar city road segment have been able to obtain vehicle speed data and are able to show traffic conditions directly with four categories of traffic conditions, namely black lines indicating traffic jammed with vehicle speeds of less than 17 km / h, red indicates heavy with speed vehicles 17 to 27 km / h, yellow indicates medium speed with vehicles 26 km/h to 40 km / h and green shows fluent with vehicle speeds above 40 km / h.

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