Research on road traffic congestion index based on comprehensive parameters: Taking Dalian city as an example

Abstract

Traffic congestion index reflects the state of traffic flow. The detection and analysis on traffic congestion index can be used to estimate the operation status of roads, to plan and organize road traffic for traffic managers, and to make the reasonable decisions of travelers to travel. The traffic conditions of several evaluation indexes were analyzed. Based on the theory of fuzzy mathematics, some membership functions of the evaluating indexes were designed. Three calculation methods of traffic congestion index were proposed. Their calculation results were compared mutually. The conclusion revealed that using saturation calculated by the corresponding service level of traffic congestion index not well reflect the traffic situation, what’s more, travel speed is used to calculate the congestion index of the first method. Using comprehensive parameters can calculate the congestion index of the third method. Both them are roughly similar and in line with the actual traffic phenomenon.

Predicting Traffic Flow Propagation Based on Congestion at Neighbouring Roads Using Hidden Markov Model

“…Traffic congestion rank was defined by Rothenberg [27] as the condition in which the number of vehicles on the road surpasses the carrying capacity of the standard road service level. Another study used congestion index by considering the saturation degree, travelling speed and a combination of both [28]. A different study accounted for the speed performance index by segmenting congestion level as four, three or two as needed [29].…”

“…Traffic flow Congestion index was calculated based on the travelling speed [28], with some adjustments. Instead of hourly calculation, congestion index was calculated every 20 minutes using Eq.…”

Google Traffic sebagai Masukan Kebijakan Transportasi Perkotaan (Studi Kasus: Kota Bandar Lampung)

“…Metode ini membantu pemangku kebijakan dalam melihat singkat bagaimana kondisi kemacetan dan efektifitas kebijakan yang telah dilakukan. Apabila dibandingkan dengan metode MKJI (Koloway, 2009) (Novalia, Sulistiyorini, & Putra, 2016) (Astuti, 2015) dalam menganalisa kemacetan ruas jalan yang membutuhkan waktu dan biaya yang besar, dengan metode analisa model google traffic akan membantu menilai kemacetan suatu area dengan efektif meskipun tidak seakurat metode MKJI ataupun (Suresh, 2018) (Wang, 2018) (Ye, 2012) (Zhang, 2008) (Mallinckrodt, 2010).…”

“…Penelitian ini menambah metode analisa kemacetan jalan dengan metode yang efektif disamping metode lainnya yaitu Algoritma C4.5, Google Maps API, dan metode MKJI. (Ye, 2012) (Wang, 2018) (Maji, 2017) DAFTAR PUSTAKA Adisasmita, R. (2006). Pembangunan Pedesaan dan Perkotaan.…”

Fuzzy-based quantification of congestion for traffic control

“…Quantification of congestion level is essential for congestion management. So far, efforts have been made to find ways to evaluate congestion level on roadway segments: For examples: [1] used density as a parameter to describe operational conditions for basic freeway segments; [2] evaluated the traffic congestion on urban highways based on speed, travel time, and demand/capacity ratio; [3] proposed several indicators to evaluate traffic congestion, including average travel speed, road saturation degree, loss of speed, and low-speed proportion.…”