GPS navigation apps and the price of anarchy

2020 ◽  
Vol 104 (560) ◽  
pp. 235-240
Author(s):  
Leonard M. Wapner
Keyword(s):  
Real Time ◽  
Traffic Accidents ◽  
Price Of Anarchy ◽  
Heavy Traffic ◽  
Road Construction ◽  
Gps Navigation ◽  
Traffic Conditions ◽  
Real Time Traffic ◽  
Road Maps ◽  
Drive Time

Folded paper road maps are found next to sextants in the pile of obsolete navigation tools. GPS navigation apps like Waze and Google Maps, accurate to within a few metres, are available on all smartphones and most new cars. These apps provide drivers with real time traffic conditions and suggest minimum drive time routes, giving drivers the ability to avoid congestion and delays caused by heavy traffic, accidents, road construction and other hindrances.

scholarly journals Deep Deconvolution for Traffic Analysis with Distributed Acoustic Sensing Data

2021 ◽  
Author(s):  
Martijn van den Ende ◽  
André Ferrari ◽  
Anthony Sladen ◽  
Cédric Richard
Keyword(s):  
Real Time ◽  
Temporal Resolution ◽  
Heavy Traffic ◽  
Simultaneous Detection ◽  
Detection Accuracy ◽  
Acoustic Sensing ◽  
Traffic Conditions ◽  
Real Time Traffic ◽  
Vibration Sensing ◽  
Distributed Acoustic Sensing

Distributed Acoustic Sensing (DAS) is a novel vibration sensing technology that can be employed to detect vehicles and to analyse traffic flows using existing telecommunication cables. DAS therefore has great potential in future "smart city" developments, such as real-time traffic incident detection. Though previous studies have considered vehicle detection under relatively light traffic conditions, in order for DAS to be a feasible technology in real-world scenarios, detection algorithms need to also perform robustly under heavy traffic conditions. In this study we investigate the potential of roadside DAS for the simultaneous detection and characterisation of the velocity of individual vehicles. To improve the temporal resolution and detection accuracy, we propose a self-supervised Deep Learning approach that deconvolves the characteristic car impulse response from the DAS data, which we refer to as a Deconvolution Auto-Encoder (DAE). We show that deconvolution of the DAS data with our DAE leads to better temporal resolution and detection performance than the original (non-deconvolved) data. We subsequently apply our DAE to a 24-hour traffic cycle, demonstrating the feasibility of our proposed method to process large volumes of DAS data, potentially in near-real time.

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 Research on Optimized Control Model of Freeway Based on Dynamic Traffic Demand Estimation

2014 ◽  
Vol 6 ◽  
pp. 797293 ◽  
Author(s):  
Zhu Jiang ◽  
Shubin Li
Keyword(s):  
Optimal Control ◽  
Real Time ◽  
Demand Estimation ◽  
Control Model ◽  
Control Module ◽  
Traffic Demand ◽  
The Real ◽  
Traffic Conditions ◽  
Real Time Traffic ◽  
Global Optimal

According to the estimation information of dynamic traffic demands, a novel optimal control model of freeway was established on the basis of the hierarchical concept. There are four control modules in this model. The OD prediction module predicts the total traffic demands in a long time and determines the upper bound of the future queuing length in advance; the global optimal control module predicts the future traffic state and establishes the coordination constraints for each ramp in the network; the traffic demand estimation module estimates the real-time traffic conditions for each ramp; the local adaptive control module regulates ramp metering rate according to the estimated information of the real-time traffic conditions and the results optimized by the global optimal control module. The simulation results show that this control system is of a good dynamic performance. It coordinates the benefits of various ramps and optimizes the overall performance of the freeway network.

Driver Acceptance of Unreliable Route Guidance Information

1994 ◽  
Vol 38 (16) ◽  
pp. 1062-1066 ◽  
Author(s):  
Richard J. Hanowski ◽  
Susan C. Kantowitz ◽  
Barry H. Kantowitz
Keyword(s):  
Real Time ◽  
Heavy Traffic ◽  
Traffic Information ◽  
Route Guidance ◽  
Experimental Conditions ◽  
Traveler Information ◽  
Traveler Information Systems ◽  
Traffic Conditions ◽  
Driver Acceptance ◽  
Real Time Information

Human factors research can be used to design safe and efficient Advanced Traveler Information Systems (ATIS) that are easy to use (Kantowitz, Becker, & Barlow, 1993). This research used the Battelle Route Guidance Simulator (RGS) to examine two important issues related to driver behavior and acceptance of ATIS technology: (1) the effect of route familiarity on ATIS use and acceptance and (2) the level of information accuracy needed for an ATIS to be accepted and considered useful. The RGS included two 486 computers that provided drivers with real-time information and traffic reports. Drivers used a touch screen to select routes on one computer monitor and watched the results of their selection (i.e., real-time video of the traffic) on a second computer monitor. Drivers could use the system to obtain information about the traffic conditions on any link before traversing a route. In this experiment, subjects were exposed to four experimental conditions involving manipulation of the driver's familiarity with the route and the reliability of the traffic information obtained from the RGS (i.e., 100%, 71%, and 43% accuracy). The driver's goal was to reach the destination as quickly as possible by avoiding heavy traffic. The results indicated that drivers were able to benefit from system information when it was reliable, but not when it was unreliable. Trust ratings for the 43% accuracy group were significantly higher at the beginning of the four trials than at the end. Also, drivers were more apt to rely on the ATIS and accept information given in an unfamiliar traffic network versus a familiar one.

scholarly journals An Empirical Study for Green Transportation Scheme of Municipal Solid Waste Based on Complex Data Model Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Chunlin Xin ◽  
Lingjie Wang ◽  
Bin Liu ◽  
Yu-Hsi Yuan ◽  
Sang-Bing Tsai
Keyword(s):  
Air Pollution ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Real Time ◽  
Time Window ◽  
Complex Data ◽  
Vehicle Speed ◽  
Traffic Conditions ◽  
Real Time Traffic ◽  
Green Transportation

Solid waste management and air pollution are two pressing issues in the functioning of large cities. This paper studies the optimization problem of the green transportation route of municipal solid waste and establishes a mathematical planning model based on real-time traffic conditions of the city and consideration of a time window and multiple transfer stations with the goal of minimizing energy consumption. In the optimal green transportation process in this paper, comprehensive consideration of vehicle speed, vehicle load, road gradient, and driving distance in different road sections based on real-time traffic conditions is incorporated, which has a better fuel-saving potential than the shortest path. A green transportation program can alleviate the air pollution problem in big cities and promote energy conservation and emission reduction in solid waste transportation.

scholarly journals Dynamic Local Vehicular Flow Optimization Using Real-Time Traffic Conditions at Multiple Road Intersections

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 28137-28157 ◽  
Author(s):  
Sookyoung Lee ◽  
Mohamed Younis ◽  
Aiswarya Murali ◽  
Meejeong Lee
Keyword(s):  
Real Time ◽  
Flow Optimization ◽  
Traffic Conditions ◽  
Real Time Traffic ◽  
Road Intersections

A MACHINE LEARNING FRAMEWORK FOR REAL-TIME TRAFFIC DENSITY DETECTION

2009 ◽  
Vol 23 (07) ◽  
pp. 1265-1284 ◽  
Author(s):  
JING CHEN ◽  
EVAN TAN ◽  
ZHIDONG LI
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Density Estimation ◽  
Heavy Traffic ◽  
Weather Conditions ◽  
Traffic Density ◽  
Model Parameters ◽  
Loop Detectors ◽  
Real Time Traffic ◽  
Flow Information

Traffic flow information can be employed in an intelligent transportation system to detect and manage traffic congestion. One of the key elements in determining the traffic flow information is traffic density estimation. The goal of traffic density estimation is to determine the density of vehicles on a given road from loop detectors, traffic radars, or surveillance cameras. However, due to the inflexibility of deploying loop detectors and traffic radars, there is a growing trend of using video-content-understanding technique to determine the traffic flow from a surveillance camera. But difficulties arise when attempting to do this in real-time under changing illumination and weather conditions as well as heavy traffic congestions. In this paper, we attempt to address the problem of real-time traffic density estimation by using a stochastic model called Hidden Markov Models (HMM) to probabilistically determine the traffic density state. Choosing a good set of model parameters for HMMs has a significant impact on the accuracy of traffic density estimation. Thus, we propose a novel feature extraction scheme to represent traffic density, and a novel approach to initialize and construct the HMMs by using an unsupervised clustering technique called AutoClass. We show through extensive experiments that our proposed real-time algorithm achieves an average traffic density estimation accuracy of 96.6% over various different illumination and weather conditions.

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