scholarly journals Context-specific volume–delay curves by combining crowd-sourced traffic data with automated traffic counters: A case study for London

2020 ◽  
Vol 1 ◽  
Author(s):  
Gerard Casey ◽  
Bingyu Zhao ◽  
Krishna Kumar ◽  
Kenichi Soga
Keyword(s):  
Traffic Congestion ◽  
Specific Volume ◽  
Traffic Volume ◽  
Ease Of Use ◽  
General Nature ◽  
Volume Data ◽  
Traffic Condition ◽  
Delay Function ◽  
Traffic Speed ◽  
Context Specific

Abstract Traffic congestion across the world has reached chronic levels. Despite many technological disruptions, one of the most fundamental and widely used functions within traffic modeling, the volume–delay function has seen little in the way of change since it was developed in the 1960s. Traditionally macroscopic methods have been employed to relate traffic volume to vehicular journey time. The general nature of these functions enables their ease of use and gives widespread applicability. However, they lack the ability to consider individual road characteristics (i.e., geometry, presence of traffic furniture, road quality, and surrounding environment). This research investigates the feasibility to reconstruct the model using two different data sources, namely the traffic speed from Google Maps’ Directions Application Programming Interface (API) and traffic volume data from automated traffic counters (ATC). Google’s traffic speed data are crowd-sourced from the smartphone Global Positioning System (GPS) of road users, able to reflect real-time, context-specific traffic condition of a road. On the other hand, the ATCs enable the harvesting of the vehicle volume data over equally fine temporal resolutions (hourly or less). By combining them for different road types in London, new context-specific volume–delay functions can be generated. This method shows promise in selected locations with the generation of robust functions. In other locations, it highlights the need to better understand other influencing factors, such as the presence of on-road parking or weather events.

scholarly journals Analysis of congestion cost at signalized intersection using Vissim 9 (Case study at Demak Ijo Intersection, Sleman)

2018 ◽  
Vol 181 ◽  
pp. 06001
Author(s):  
Noor Mahmudah ◽  
Rizkie Akbar ◽  
Muchlisin
Keyword(s):  
Traffic Congestion ◽  
Traffic Volume ◽  
Signalized Intersection ◽  
Average Delay ◽  
Traffic Condition ◽  
The Road ◽  
Congestion Cost ◽  
Road Capacity ◽  
High Traffic Volume

Due to imbalance of road capacity and traffic volume, so traffic congestion will be occurred either along the road and intersection. Demak Ijo intersection is one of signalized junction located in the western part of Yogyakarta with high traffic volume so traffic congestion is frequently occurred. The aim of this study is to analyze the performance of existing traffic condition and then estimate the congestion cost at signalized intersection by modeling (simulation) using Vissim 9. The analysis results show that existing traffic condition is in very bad condition (level F), average delay of 80 seconds, average queue length of 48.73 meters with congestion cost is about Rp. 2,830,336 per hour.

scholarly journals Ensemble Learning for Short-Term Traffic Prediction Based on Gradient Boosting Machine

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Senyan Yang ◽  
Jianping Wu ◽  
Yiman Du ◽  
Yingqi He ◽  
Xu Chen
Keyword(s):  
Ensemble Learning ◽  
Traffic Volume ◽  
Prediction Performance ◽  
Traffic Prediction ◽  
Gradient Boosting ◽  
Relative Importance ◽  
Volume Data ◽  
Short Term ◽  
Traffic Condition ◽  
Input Variables

Short-term traffic prediction is vital for intelligent traffic systems and influenced by neighboring traffic condition. Gradient boosting decision trees (GBDT), an ensemble learning method, is proposed to make short-term traffic prediction based on the traffic volume data collected by loop detectors on the freeway. Each new simple decision tree is sequentially added and trained with the error of the previous whole ensemble model at each iteration. The relative importance of variables can be quantified in the training process of GBDT, indicating the interaction between input variables and response. The influence of neighboring traffic condition on prediction performance is identified through combining the traffic volume data collected by different upstream and downstream detectors as the input, which can also improve prediction performance. The relative importance of input variables for 15 GBDT models is different, and the impact of upstream traffic condition is not balanced with that of downstream. The prediction accuracy of GBDT is generally higher than SVM and BPNN for different steps ahead, and the accuracy of multi-step-ahead models is lower than 1-step-ahead models. For 1-step-ahead models, the prediction errors of GBDT are smaller than SVM and BPNN for both peak and nonpeak hours.

A Deep Learning Model for Off-Ramp Hourly Traffic Volume Estimation

2021 ◽  
pp. 036119812110271
Author(s):  
Amir Nohekhan ◽  
Sara Zahedian ◽  
Ali Haghani
Keyword(s):  
Deep Learning ◽  
Transportation Network ◽  
Flow Characteristics ◽  
Learning Model ◽  
Traffic Volume ◽  
Volume Estimation ◽  
Primary Data ◽  
Volume Data ◽  
Traffic Condition ◽  
Deep Learning Model

This paper addresses estimation of traffic volume of freeway off-ramps. Freeways are the transportation network’s main corridors, serving a large portion of the traffic volume. This traffic passes into the lower-level roads through off-ramps. Therefore, the traffic condition of the off-ramps is an essential factor affecting the operation of the transportation network. The continuous collection of volume data is impractical, and transportation authorities install vehicle detectors permanently on only a few off-ramps and temporarily (e.g., a week) on some others. Thus, traffic volume is the most challenging to estimate among various traffic measures. Moreover, the existing literature on volume estimation is mainly concerned with evaluating traffic on the main road segments. However, the distinct characteristics of the connection links, such as off-ramps, demands specified modeling. This study estimates the hourly traffic volume of off-ramps using a deep learning model. It evaluates the advantages of inputting the connected lower-level road features to the model, and explores various detector installation strategies on the model training process. The primary data sources are volume counts, probe speeds, and road segment infrastructure characteristics. The model results indicate that the incorporation of traffic flow characteristics and infrastructure attributes of the lower-level road connected to the freeway significantly improves the accuracy of estimation off-ramp traffic volume. Further, analysis illustrated that the model trained with data from temporarily installed detectors on all interchanges outperformed models trained with permanently installed detectors on 90% of the interchanges, indicating the model’s ability in extracting temporal correlations significantly more than spatial correlations.

scholarly journals Effect of Well-being on People Surrounding the Airport Corridor using Predictive Analysis on Road Accident Correlation

2021 ◽  
Vol 9 (6) ◽  
pp. 121-126
Author(s):  
Jotirmay Chari ◽  
Shankar B.
Keyword(s):  
Traffic Congestion ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Police Department ◽  
Well Being ◽  
Road Accident ◽  
Traffic Volume ◽  
Road Traffic Accidents ◽  
Volume Data ◽  
Road Planning

Transportation demands in urban regions continue to upsurge due to population growth and travel modes’ alterations. Due to Bangalore airport location and improper road planning, there is an increase in the traffic volume, which leads to traffic congestion and road traffic accidents in the city. The present study analyses the effect of well-being on the airport corridor residents based on road traffic accidents, traffic volume, and road design. The study collected the traffic accident data from the Traffic Police department for the period from 2014-2015 to 2018-2019, and traffic volume data collected from Essel Devanhalli Tollway Pvt Ltd (EDTPL) for the similar period was analyzed. The study found a significant relationship between improper road pl Manuscript | Research Paper anning, increased traffic volume, and road traffic accidents. The study could be used for road planning as well as better traffic management.

Analisis Kinerja dan Pengaruh Pelebaran Jalan serta Pembuatan Median Jalan (Studi Kasus Jalan Temanggung Tilung Kota Palangkaraya)

2018 ◽  
Vol 7 (1) ◽  
pp. 51-60
Author(s):  
Fitri Wulandari ◽  
Nirwana Puspasari ◽  
Noviyanthy Handayani
Keyword(s):  
Data Retrieval ◽  
Service Level ◽  
Traffic Volume ◽  
Primary Data ◽  
Volume Data ◽  
Saturation Degree ◽  
The Road ◽  
Before And After ◽  
Road Widening ◽  
The Way

Jalan Temanggung Tilung is a 2/2 UD type road (two undirected two-way lanes) with a road width of 5.5 meters, which is a connecting road between two major roads, namely the RTA road. Milono and the path of G. Obos. Over time, the volume of traffic through these roads increases every year, plus roadside activities that also increase cause congestion at several points of the way. To overcome this problem, the local government carried out road widening to increase the capacity and level of road services. The study was conducted to determine the amount of traffic volume, performance, service level of the Temanggung Tilung road section at peak traffic hours before and after road widening. Data retrieval is done by the direct survey to the field to obtain primary data in the form of geometric road data, two-way traffic volume data, and side obstacle data. Performance analysis refers to the 1997 Indonesian Road Capacity Manual (MKJI) for urban roads. From the results of data processing, before increasing the road (Type 2/2 UD), the traffic volume that passes through the path is 842 pcs/hour and after road widening (Type 4/2 UD) the traffic volume for two directions is 973 pcs/hour, with route A equaling 528 pcs/hour and direction B equaling 445 pcs/hour. Based on the analysis of road performance before road enhancement, the capacity = 2551 pcs/hour, saturation degree = 0.331, and the service level of the two-way road are level B. Based on the analysis of the performance of the way after increasing the way, the direction capacity A = 2686 pcs/hour and direction B = 2674 pcs /hour, saturation degree for direction A = 0.196 and direction B = 0.166, service level for road direction A and direction B increase to level A

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

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

<p>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 <em>(real time)</em> 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 <em>smartphone</em>, untuk informasi kondisi lalu lintas secara langsung. GPS <em>Receiver</em> pada <em>smartphone</em> 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 <em>smartphone</em>, 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.</p><p> </p><p><em><strong>Abstract</strong></em></p><p class="Abstract"><em>The 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.</em></p><p><em><strong><br /></strong></em></p>

scholarly journals A framework for IoT-enabled environment aware traffic management

2021 ◽  
Vol 11 (1) ◽  
pp. 518
Author(s):  
Mohamed Fazil Mohamed Firdhous ◽  
B. H. Sudantha ◽  
Naseer Ali Hussien
Keyword(s):  
Traffic Congestion ◽  
Urban Areas ◽  
Traffic Management ◽  
Fine Particles ◽  
Environment Pollution ◽  
Negative Effects ◽  
Traffic Condition ◽  
Pollution Levels ◽  
Transport Services ◽  
The Cost

Vehicular traffic has increased across all over the world especially in urban areas due to many reasons including the reduction in the cost of vehicles, degradation of the quality of public transport services and increased wealth of people. The traffic congestion created by these vehicles causes many problems. Increased environment pollution is one of the most serious negative effects of traffic congestion. Noxious gases and fine particles emitted by vehicles affect people in different ways depending on their age and present health conditions. Professionals and policy makers have devised schemes for better managing traffic in congested areas. These schemes suffer from many shortcomings including the inability to adapt to dynamic changes of traffic patterns. With the development of technology, new applications like Google maps help drivers to select less congested routes. But, the identification of the best route takes only the present traffic condition on different road segments presently. In this paper the authors propose a system that helps drivers select routes based on the present and expected environment pollution levels at critical points in a given area.

scholarly journals Accurate Real-Time Traffic Speed Estimation Using Infrastructure-Free Vehicular Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-19 ◽  
Author(s):  
Zongjian He ◽  
Buyang Cao ◽  
Yan Liu
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Vehicular Networks ◽  
Route Planning ◽  
Speed Estimation ◽  
Traffic Condition ◽  
Real Time Traffic ◽  
Traffic Trace ◽  
Traffic Speed ◽  
Traffic Speed Estimation

Real-time traffic speed is indispensable for many ITS applications, such as traffic-aware route planning and eco-driving advisory system. Existing traffic speed estimation solutions assume vehicles travel along roads using constant speed. However, this assumption does not hold due to traffic dynamicity and can potentially lead to inaccurate estimation in real world. In this paper, we propose a novel in-network traffic speed estimation approach using infrastructure-free vehicular networks. The proposed solution utilizes macroscopic traffic flow model to estimate the traffic condition. The selected model only relies on vehicle density, which is less likely to be affected by the traffic dynamicity. In addition, we also demonstrate an application of the proposed solution in real-time route planning applications. Extensive evaluations using both traffic trace based large scale simulation and testbed based implementation have been performed. The results show that our solution outperforms some existing ones in terms of accuracy and efficiency in traffic-aware route planning applications.

scholarly journals ANALISIS FAKTOR KEMACETAN TERHADAP KINERJA LALU LINTAS DI RUAS JALAN JENDERAL BASUKI RAHMAT (STUDI KASUS: MALL BASSURA)

LOGISTIK ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 12-18
Author(s):  
Hafidzsyah Alfiana ◽  
Adhi Purnomo
Keyword(s):  
Service Level ◽  
Traffic Volume ◽  
Volume Data ◽  
Street Vendors ◽  
The Road ◽  
Traffic Performance ◽  
Field Data Collection ◽  
Road Performance ◽  
Observation Method ◽  
Wheeled Vehicles

Commercial buildings in the Bassura Mall area (Jalan Jenderal Basuki Rahmat) have resulted in congestion in the area. Vehicles use road bodies to park their vehicles, sidewalks that are used as trading places by street vendors, and the position of the entrance / exit of Bassura Mall for four-wheeled vehicles that is not strategic. These things have caused the author to choose a location in the Bassura Mall area (Jalan Jenderal Basuki Rahmat), East Jakarta which is a local route, where East Jakarta as one of the big cities in the Special Capital Region of Jakarta also experiences high vehicle growth, p. This can be seen on roads in East Jakarta which often experience congestion during rush hour. The purpose of this research is to determine the traffic performance in the area. The method used in this research is direct observation method at the research location and field data collection in the form of parking volume data, pedestrian volume, vehicle volume entering / exiting Mall Bassura, traffic volume, and road performance. Observations were made on weekdays and holidays. The results of the analysis of the performance of the road sections show that the traffic volume on the roads around Mall Bassura is 32,433 SMP / hour on weekdays and 22,577 SMP / hour on holidays, with the service level index being in the F category on weekdays and holidays. Thus, the congestion factors mentioned above have resulted in a decrease in road capacity which has an impact on road performance.

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