Vehicle Talks to IoT for Better Driving Experience

Internet of vehicles (IoV), or vehicles to everything (V2X), is a relatively new concept for all vehicles connected to the internet. In this paper, we propose a solution to the problems of traffic congestion. Road traffic congestion has continued to be a major problem in many developed countries. This concept shows the place or location traffic congestion occurs at the time of rainfall, the position of the data based on GPS, and the sensors in the vehicle. The sensors in vehicles are used to collect information for the purpose of evaluating traffic congestion through cloud server via protocols such as CoAP and MQTT. At the beginning of the study, the authors introduced the problems and then proposed a solution

The Influence of Jogjakarta Outer Ring Road Development Plan on the National Roads in the Special Region of Yogyakarta

The Influence of Jogjakarta Outer Ring Road Development Plan on the National Roads in the Special Region of Yogyakart

Analysis of Traffic Congestion on Lagos/Abeokuta Expressway-Agege Motorway in Lagos Metropolis

Traffic congestion is one of the most persistent problems facing road users and urban planners, across the world and Lagos is not an exception. This study analysed traffic congestion along the Lagos/Abeokuta expressway-Agege motorway in Lagos metropolis. It explored the volume of vehicular traffic (VVT) along the road corridor, conducted a road network analysis, and investigated the causative factors of traffic congestion on the road corridor. Traffic count data of vehicles plying the road between 7 a.m.-8 p.m. was obtained from Lagos Metropolitan Area Transport Authority (LAMATA) to aid the assessment of VVT. Graph theory-based network index was used in determining road connectivity level, and a cross sectional survey of 384 commuters was conducted to obtain information on traffic congestion along the road corridor. Results of the analysis indicated that the VVT is higher at Ikeja (27.4%) than other locations such as (Alimosho 21.5%, Oshodi 16.2%, etc.). Road network analysis showed high connectivity of Lagos/Abeokuta expressway-Agege motorway with gamma, alpha, and beta indexes (0.8, 0.83 & 2.67) respectively, indicating that road connectivity is not a cause of congestion on the corridor. However, the causative factors of traffic congestion include; overdependence on road, shortage of traffic light, insufficient number of traffic warden, and disobeying of traffic laws (p < 0.05). In conclusion, overdependence on road is the major cause of congestion in the metropolis. Thus, there is an urgent need to improve alternative transport modes in the Lagos metropolis. Keywords: traffic congestion, road network analysis, overdependence on roa

ICT and COMPRAM to assess road traffic congestion management in Kinshasa

Abstract: Traffic Congestion Management (TCM) in a megacity like Kinshasa, capital of the DR Congo, is a knowledge and real life problem of complex nature. Here, the authors describe the TCM problem through 9 phases of the layer 1 of the COMPRAM methodology. TCM is a worldwide complex societal problem and specifically in Kinshasa where it presents a set of characteristics such as ‘chaotic’ driver behaviour, road potholes and the road network physiognomy doesn’t respond to the supply- versus demand-side equation. The other complex problems include the absence of road planning with consideration to demographic parameters and car ownership increase, no suitable traffic operations infrastructure and limited funds for both maintaining existing roads and building additional ones. To solve this TCM problem, the authors propose a TTCMP (Triangular Traffic Congestion Management Process) framework as an output based layer 1 of COMPRAM by identifying types and sources of congestion, followed by a TCM problem description and a set of technical elements for ‘curbing’ traffic congestion with an overview on a Bluetooth based technology for traffic data collection as an adapted ICT4D solution for a low-income city like Kinshasa

Adaptive traffic light cycle time controller using microcontrollers and crowdsource data of Google APIs for developing countries

Mishra, S., Bhattacharya, D., Gupta, A., & Singh, V. R. (2018). Adaptive traffic light cycle time controller using microcontrollers and crwodsource data of Google APIs for developing countries. In 3rd International Conference on Smart Data and Smart Cities (4/W7 ed., Vol. 4, pp. 83-90). (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences). DOI: 10.5194/isprs-annals-IV-4-W7-83-2018Controlling of traffic signals optimally helps in avoiding traffic jams as vehicle volume density changes on temporally short and spatially small scales. Nowadays, due to embedded system development with the rising standards of computational technology, condense electronics boards as well as software packages, system can be developed for controlling cycle time in real time. At present, the traffic control systems in India lack intelligence and act as an open-loop control system, with no feedback or sensing network, due to the high costs involved. This paper aims to improve the traffic control system by integrating different technologies to provide intelligent feedback to the existing network with congestion status adapting to the changing traffic density patterns. The system presented in this paper aims to sense real-time traffic congestion around the traffic light using Google API crowdsource data and hence avoids infrastructure cost of sensors. Subsequently, it manipulates the signal timing by triggering and conveying information to the timer control system. Generic information processing and communication hardware system designed in this paper has been tested and found to be functional for a pilot run in real time. Both simulation and hardware trials show the transmission of required information with an average time delay of 1.2 seconds that is comparatively very small considering cycle time.publishersversionpublishe

Traffic system in Malaysia: The factors that influence traffic congestion in urban area

The focus of this paper is on traffic congestion in urban area such as Kuala Lumpur and Penang. Traffic system in Malaysia still in the low level.It is because Malaysia has a many barrier to be a good in traffic system likes other country.The factors that influence this condition occur is the attitude of the road users, service of public transport, high population and facilities.Besides that, if traffic congestion interminable occur it will give negative impact to environment and economic.Because of that, all party must involved and plays their own roles to reduce the congestion problems

Trans-Sense: Real Time Transportation Schedule Estimation Using Smart Phones

Developing countries suffer from traffic congestion, poorly planned road/rail networks, and lack of access to public transportation facilities. This context results in an increase in fuel consumption, pollution level, monetary losses, massive delays, and less productivity. On the other hand, it has a negative impact on the commuters feelings and moods. Availability of real-time transit information - by providing public transportation vehicles locations using GPS devices - helps in estimating a passenger's waiting time and addressing the above issues. However, such solution is expensive for developing countries. This paper aims at designing and implementing a crowd-sourced mobile phones-based solution to estimate the expected waiting time of a passenger in public transit systems, the prediction of the remaining time to get on/off a vehicle, and to construct a real time public transit schedule. Trans-Sense has been evaluated using real data collected for over 800 hours, on a daily basis, by different Android phones, and using different light rail transit lines at different time spans. The results show that Trans-Sense can achieve an average recall and precision of 95.35% and 90.1%, respectively, in discriminating lightrail stations. Moreover, the empirical distributions governing the different time delays affecting a passenger's total trip time enable predicting the right time of arrival of a passenger to her destination with an accuracy of 91.81%.In addition, the system estimates the stations dimensions with an accuracy of 95.71%.Comment: 8 pages, 11 figures