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

(5 citation statements)

References 1 publication

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“…A support vector regression (SVR) approach was used to predict traffic flow from California highways using different types of kernels 54 . A road network density (one of the transportation indices) prediction model was proposed using highway capacity, and turning probabilities manual methods were used to determine the shortest cycling time in metropolitan areas 55 . The model could aid in vehicle distribution and congestion relief in urban areas.…”

Section: Transportation and Urban Landscape Structuresmentioning

confidence: 99%

Prediction of transportation index for urban patterns in small and medium-sized Indian cities using hybrid RidgeGAN model

Thottolil,

Kumar,

Chakraborty

2023

Sci Rep

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The rapid urbanization trend in most developing countries including India is creating a plethora of civic concerns such as loss of green space, degradation of environmental health, scarcity of clean water, rise in air pollution, and exacerbated traffic congestion resulting in significant delays in vehicular transportation. To address the intricate nature of transportation issues, many researchers and planners have analyzed the complexities of urban and regional road systems using transportation models by employing transportation indices such as road length, network density, accessibility, and connectivity metrics. This study addresses the complexities of predicting road network density for small and medium-sized Indian cities that come under the Integrated Development of Small and Medium Towns (IDSMT) project at a national level. A hybrid framework based on Kernel Ridge Regression (KRR) and the CityGAN model is introduced to predict network density using spatial indicators of human settlements. The major goal of this study is to generate hyper-realistic urban patterns of small and medium-sized Indian cities using an unsupervised CityGAN model and to study the causal relationship between human settlement indices (HSIs) and transportation index (network density) using supervised KRR for the real cities. The synthetic urban universes mimic Indian urban patterns and evaluating their landscape structures through the settlement indices can aid in comprehending urban landscape, thereby enhancing sustainable urban planning. We analyzed 503 real cities to find the actual relationship between the urban settlements and their road density. The nonlinear KRR model may help urban planners in deriving the network density for GAN-generated futuristic urban patterns through the settlement indicators. The proposed hybrid process, termed as RidgeGAN model, can gauge the sustainability of urban sprawl tied to infrastructure and transportation systems in sprawling cities. Analysis results clearly demonstrate the utility of RidgeGAN in predicting network density for different kinds of human settlements, particularly for small and medium Indian cities. By predicting future urban patterns, this study can help in the creation of more livable and sustainable areas, particularly by improving transportation infrastructure in developing cities.

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Section: Transportation and Urban Landscape Structuresmentioning

confidence: 99%

Prediction of transportation index for urban patterns in small and medium-sized Indian cities using hybrid RidgeGAN model

Thottolil,

Kumar,

Chakraborty

2023

Sci Rep

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“…Literatürde, trafik yoğunluğunun azaltılması ile ilgili yapılan çalışmalarda; dijital görüntü işleme, sensörler, çeşitli algoritmalar, VANET, yapay sinir ağları, nesnelerin interneti yöntemlerinin en çok kullanılan yöntemler arasında olduğu görülmektedir [15][16][17][18][19][20][21][22][23][24][25]. Trafik yoğunluğunun tespiti üzerine yapılan mevcut çalışmalar temelde yol güzergahı boyunca oluşan yoğunluk dikkate alınarak sürücülere yeni rota önerildiği durumları incelemektedir [26][27][28]. Bunun yanı sıra, incelenen bazı çalışmalarda şerit değiştirme modelleri önerilerek trafik yönetimi ve seyahat süresinin optimizasyonunun sağlanması da amaçlanmıştır [11,[29][30][31].…”

Section: Li̇teratür Taramasi (Literature Review)unclassified

Determining the General Infrastructure of Lane Density Detection and Information Systems from Design to Operation: An Example System Design

AYDIN,

BIYIK

2023

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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Dünya’da ve Türkiye’de trafiğe çıkan araç sayısı gün geçtikçe artmaktadır. Bu durum doğal olarak trafikte oluşan araç yoğunluğunun giderek artmasına ve yolculuklarda önemli gecikmelere sebebiyet verebilmektedir. Birçok insanın günlük hayatını etkileyen bu sorun, özellikle nüfus yoğunluğunun fazla olduğu şehirlerde kendini daha net göstermektedir. Şehiriçi trafikte oluşan yoğunluğun çeşitli sebeplerinin olmasının yanı sıra, trafikte araç sürücülerinin şerit seçim ve kullanım tercihleri de oluşan yoğunluk üzerinde önemli olumsuz bir etkiye sahip olabilmektedir. Sürücülerin yollarda en sol şerit daha hızlı hareket eder düşüncesi ile o şeridi daha çok kullanma arzusu içerisinde olması, tek bir şeritte yığılma oluşmasına neden olabilmektedir. Çalışmada, akıllı ulaşım sistemleri yardımıyla çok şeritli şehiriçi yollarda şerit kullanım yoğunluklarının tespitinin yapılmasını amaçlayan yenilikçi bir akıllı sistem öneri tasarımı sunulmuştur. Önerilen sistem üzerinde etkili olan faktörlerin belirlenmesi amacıyla SWOT analizi yapılmıştır. SWOT analizi ile belirlenen faktörler, beş kişilik bir uzman ekibin değerlendirmeleri ile analitik hiyerarşi prosesi (AHP) yöntemi kullanılarak ağırlıklandırılmıştır. Uygulanan A’WOT analizinden elde edilen sonuçlara göre sistemin en güçlü yönünün önerilen bu sistemden arzulanan doğru şerit kullanımı ile trafikte tıkanıklığın azaltılması olduğu görülmüştür. Önerilen sistemin çıkış noktası olan bu faktörün ön plana çıkması, çalışmanın temel amacını desteklemiş ve bu tür yenilikçi sistemlere ihtiyaç olduğunu net şekilde ortaya koymuştur.

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“…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

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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.

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

Cited by 6 publications

References 1 publication

Prediction of transportation index for urban patterns in small and medium-sized Indian cities using hybrid RidgeGAN model

Prediction of transportation index for urban patterns in small and medium-sized Indian cities using hybrid RidgeGAN model

Determining the General Infrastructure of Lane Density Detection and Information Systems from Design to Operation: An Example System Design

On Identifying Dynamic Intersections in Large Cities

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