Road density prediction: Updated methods of turning probabilities and highway capacities manual for achieving the best route

Budiarto, Jian; Sulistyo, Selo; Mustika, I Wayan; Infantono, Ardian

doi:10.1109/iceecs.2014.7045239

Cited by 6 publications

(3 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This has been done in [21], where using those probabilities a road density prediction method is described. It should be noted that most methods found in literature are concerned with analysing and simulating intersections for rush hour conditions, while neglecting the temporal nature of traffic, the change that the flows might exhibit throughout the day and their effects.…”

Section: Literature Reviewmentioning

confidence: 99%

On Identifying Dynamic Intersections in Large Cities

Ivanchev¹,

Aydt²,

Knoll

2015

2015 IEEE 18th International Conference on Intelligent Transportation Systems

View full text Add to dashboard Cite

Abstract-As the complexity of traffic conditions in large cities increases it becomes important and highly desirable to be able to analyse the spatial and temporal nature of such systems. Due to the heterogeneity of traffic demand and road network topology [1], it is possible to find "hot spots" in a network that present a challenge for city planning and conventional intersection control methods. This paper presents an approach to identify such places as physical intersections in a traffic network with dynamically changing demand conditions in time and to quantify the level of volatility at those locations. We design a model that is used to simulate commuters path choices using a stochastic routing approach. We perform a case study for the city of Singapore and calibrate our model with national survey data describing the travel habits of the population. The results from our simulation are used to analyse the traffic conditions in the city. We are able to identify and study highly dynamic intersections and observe that such locations in fact exist and contribute to the heterogeneous dynamic profile of the road network.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

On Identifying Dynamic Intersections in Large Cities

Ivanchev¹,

Aydt²,

Knoll

2015

2015 IEEE 18th International Conference on Intelligent Transportation Systems

View full text Add to dashboard Cite

show abstract

“…One of the them is traffic congestion. Traffic congestion commonly occurs in areas with high population density and a high percentage of vehicles [2]. This high level of traffic congestion has brought many negative effects for society, either financial, time, or physiological losses.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Traffic Light Control Based on Actual Condition Using Google Map API

Arkanuddin

Sulistyo

Persada

2019

IJITEE

Self Cite

View full text Add to dashboard Cite

Traffic congestion is one of the main problems in transportation sector and it causes a lot of drawbacks to public. The traffic light system is used to reduce the level of occurring traffic congestion. Generally, the available traffic light systems use a fixed time setting. This old traffic control system is no longer able to manage the ever-changing traffic conditions effectively and efficiently, causing a long queue of vehicles. To overcome this problem, a traffic light control system that can adapt to actual conditions of road density and can run automatically is offered. This system utilizes Google Map API as a road density data source. The result of this study is a traffic control system that can adjust the green light time duration based on the obtained density values and density trends, simulation of this adaptive system as well as simulation results analysis. A prototype of this adaptive control system was also produced in this study.

show abstract

“…Their performance can be modelled in various ways depending if they are unsignalised [5], [6] or use a control strategy such as traffic lights [7]. Modelling approaches include the use of Kirchhoff's law for traffic flows at nodes [8], queue dynamics as a function of demands [9] or turning probabilities [10], or modelling the routing choices [11].…”

Section: Introductionmentioning

confidence: 99%

Spatial and Temporal Analysis of Mismatch between Planned Road Infrastructure and Traffic Demand in Large Cities

Ivanchev¹,

Aydt²,

Knoll³

2015

2015 IEEE 18th International Conference on Intelligent Transportation Systems

View full text Add to dashboard Cite

Abstract-As large cities evolve in time, the traffic demand and the road network adapt to the mutually presented changes by each other. As a result of this process, previously planned roads and intersections that were designed according to some optimality criteria at the time, turn out to be suboptimal when traffic conditions change. This paper presents a method that can be used in order to identify intersections whose capacity is no longer in correspondence with the demand of vehicles on them and the choices agent make at those locations. Using real data from a survey describing the travel patterns of people in the city of Singapore we are able to model the routing choices of commuters and simulate the traffic demands on the road network. After calculating the turning probabilities on every intersection we are able to compare the traffic demand for every turn with the planned physical roads' capabilities. Furthermore we define a measure, which quantifies the deviation of the whole road network from the ideal demand-calculated values. We use these measures to evaluate the temporal and spatial profile of the mismatch between the roads and the demand for them. The measure is designed such that it is universal in nature and invariant to the absolute values of the traffic flows in the city. It can, therefore, be used to compare the proper utilisation of road networks among different cities.

show abstract

Road density prediction: Updated methods of turning probabilities and highway capacities manual for achieving the best route

Cited by 6 publications

References 1 publication

On Identifying Dynamic Intersections in Large Cities

On Identifying Dynamic Intersections in Large Cities

Adaptive Traffic Light Control Based on Actual Condition Using Google Map API

Spatial and Temporal Analysis of Mismatch between Planned Road Infrastructure and Traffic Demand in Large Cities

Contact Info

Product

Resources

About