Two-Level Signalized Intersection

2008 ◽  
Vol 2060 (1) ◽  
pp. 53-64 ◽  
Author(s):  
Eon Kyo Shin ◽  
Jang Hee Lee ◽  
Ju Hyun Kim ◽  
Jung Sik Kim ◽  
Yong Woo Jeong
Keyword(s):  
Traffic Congestion ◽  
Single Point ◽  
High Volume ◽  
Complete Separation ◽  
Signalized Intersection ◽  
Traffic Conditions ◽  
Delay Times ◽  
Traffic Volumes ◽  
New Type ◽  
Intersection Types

Severe traffic congestion often occurs at intersections of high-volume traffic arteries. To reduce this congestion, innovative intersection designs such as the single-point urban interchange (SPUI), center-turn overpass (CTO), and echelon interchange (EI) have been implemented. This paper introduces a new type of intersection, the two-level signalized intersection (TLSI), which improves the efficiency of intersection operations under a wide variety of traffic conditions. The paper compares the TLSI with other innovative intersection types. Unlike the SPUI, CTO, or EI, the TLSI provides for complete separation of east–west and north–south traffic. The TLSI also enables the use of directional separation and leading, lagging, or overlapping lefts on both upper and lower levels. Simulation results indicate that, compared with these other innovative intersection types, the TLSI has the shortest delay times in most evaluation scenarios as well as the least sensitivity to variations in traffic volume. However, the TLSI shows significant delay when traffic volumes on the major and minor roads are vastly different.

Queuing and Natural Diversion at Short-Term Freeway Work Zone Lane Closures

1996 ◽  
Vol 1529 (1) ◽  
pp. 19-26 ◽  
Author(s):  
Gerald L. Ullman
Keyword(s):  
High Volume ◽  
Field Studies ◽  
Operating Conditions ◽  
Work Zone ◽  
Temporary Work ◽  
Short Term ◽  
Traffic Conditions ◽  
Traffic Stream ◽  
Traffic Volumes ◽  
Lane Closures

Research conducted to explore the effects of natural diversion on traffic conditions and travel patterns upstream of temporary work zone lane closures on high-volume urban freeways in Texas is described. Specific objectives were to explore how natural diversion affects traffic volumes at the exit and entrance ramps upstream of the lane closures and the interrelationships between the freeway and frontage road operating conditions that develop at a closure and the amount of natural diversion that occurs. The field studies showed that the rate of queue growth upstream of the short-term lane closures diminished significantly after the first hour at each site. Eventually, the queues approached a balanced state in which the upstream end of the queue became almost stationary. This stabilization was due to significant reductions in entrance ramp volumes both upstream of the freeway queue and within the limits of queuing, as well as to changes in exit ramp volumes within the queue. As a result of these ramp volume changes the constrained flow rate within the queue increased as a function of the distance upstream of the actual lane closure. Using the theory of shock waves in a traffic stream it was shown that the changes in ramp volumes and resulting impact on constrained freeway flow rates within the queue were consistent with the queue stabilization process observed at each site.

Guidelines for Selecting Single-Point Urban and Diamond Interchanges: Nationwide Survey and Literature Review

1998 ◽  
Vol 1612 (1) ◽  
pp. 48-54
Author(s):  
Michelle J. Smith ◽  
Nicholas J. Garber
Keyword(s):  
Literature Review ◽  
Traffic Congestion ◽  
Single Point ◽  
Nationwide Survey ◽  
The United States ◽  
Sources Of Information ◽  
Traffic Demand ◽  
Geometric Conditions ◽  
New Type ◽  
Diamond Interchanges

Metropolitan areas in the United States are experiencing a steep increase in traffic growth and traffic congestion on their urban freeways. In the past, the most prevalent type of urban interchange has been the diamond interchange (DI). In more recent years, however, to overcome the need for significant additional right-of-way that is generally required for this type of interchange and still respond to increased traffic demand, a new type of interchange has emerged: the single-point urban interchange (SPUI). Traffic engineers do not agree about the relative operational efficiency of the SPUI compared with the DI. Also, the unfamiliar geometry and operation of the SPUI and its large, uncontrolled conflict area have raised several concerns about the safety of motorists who travel through it. The purpose of this study was to evaluate and compare the safety and operational characteristics of the SPUI and DI and to develop guidelines that identify traffic and geometric conditions that favor one over the other. We surveyed state traffic engineers across the nation about their experiences and opinions of the two interchanges in terms of operational performance and safety. A complete literature review on the subject revealed significant findings as well. We used both of these sources of information to develop guidelines to aid traffic engineers in selecting and designing the appropriate type of interchange.

Smart Driving System for Improving Traffic Flow

2017 ◽  
Vol 7 (7) ◽  
pp. 236
Author(s):  
Rajesh Kumar Gupta ◽  
L. N. Padhy ◽  
Sanjay Kumar Padhi
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Adaptive Cruise Control ◽  
Human Perception ◽  
Cruise Control ◽  
Driving System ◽  
V2v Communication ◽  
Traffic Conditions ◽  
Cooperative Adaptive Cruise Control

Traffic congestion on road networks is one of the most significant problems that is faced in almost all urban areas. Driving under traffic congestion compels frequent idling, acceleration, and braking, which increase energy consumption and wear and tear on vehicles. By efficiently maneuvering vehicles, traffic flow can be improved. An Adaptive Cruise Control (ACC) system in a car automatically detects its leading vehicle and adjusts the headway by using both the throttle and the brake. Conventional ACC systems are not suitable in congested traffic conditions due to their response delay.  For this purpose, development of smart technologies that contribute to improved traffic flow, throughput and safety is needed. In today’s traffic, to achieve the safe inter-vehicle distance, improve safety, avoid congestion and the limited human perception of traffic conditions and human reaction characteristics constrains should be analyzed. In addition, erroneous human driving conditions may generate shockwaves in addition which causes traffic flow instabilities. In this paper to achieve inter-vehicle distance and improved throughput, we consider Cooperative Adaptive Cruise Control (CACC) system. CACC is then implemented in Smart Driving System. For better Performance, wireless communication is used to exchange Information of individual vehicle. By introducing vehicle to vehicle (V2V) communication and vehicle to roadside infrastructure (V2R) communications, the vehicle gets information not only from its previous and following vehicle but also from the vehicles in front of the previous Vehicle and following vehicle. This enables a vehicle to follow its predecessor at a closer distance under tighter control.

Regional Perspective of Safety Performance Functions and Their Application to Florida Intersections in Suburban Residential and Urban General Context Classification Categories

2021 ◽  
pp. 036119812110084
Author(s):  
Ghalia Gamaleldin ◽  
Haitham Al-Deek ◽  
Adrian Sandt ◽  
John McCombs ◽  
Alan El-Urfali
Keyword(s):  
Negative Binomial ◽  
Regression Tree ◽  
Influential Factors ◽  
Safety Performance ◽  
General Context ◽  
Boosted Regression Tree ◽  
Safety Performance Functions ◽  
Traffic Volumes ◽  
Intersection Types ◽  
The Individual

Safety performance functions (SPFs) are essential tools to help agencies predict crashes and understand influential factors. Florida Department of Transportation (FDOT) has implemented a context classification system which classifies intersections into eight context categories rather than the three classifications used in the Highway Safety Manual (HSM). Using this system, regional SPFs could be developed for 32 intersection types (unsignalized and signalized 3-leg and 4-leg for each category) rather than the 10 HSM intersection types. In this paper, eight individual intersection group SPFs were developed for the C3R-Suburban Residential and C4-Urban General categories and compared with full SPFs for these categories. These comparisons illustrate the unique and regional insights that agencies can gain by developing these individual SPFs. Poisson, negative binomial, zero-inflated, and boosted regression tree models were developed for each studied group as appropriate, with the best model selected for each group based on model interpretability and five performance measures. Additionally, a linear regression model was built to predict minor roadway traffic volumes for intersections which were missing these volumes. The full C3R and C4 SPFs contained four and six significant variables, respectively, while the individual intersection group SPFs in these categories contained six and nine variables. Factors such as major median, intersection angle, and FDOT District 7 regional variable were absent from the full SPFs. By developing individual intersection group SPFs with regional factors, agencies can better understand the factors and regional differences which affect crashes in their jurisdictions and identify effective treatments.

scholarly journals Detecting Different Road Infrastructural Elements Based on the Stochastic Characterization of Speed Patterns

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Mario Muñoz-Organero ◽  
Ramona Ruiz-Blázquez
Keyword(s):  
Total Variation ◽  
Traffic Congestion ◽  
Automatic Detection ◽  
Mass Function ◽  
Total Variation Distance ◽  
Statistical Moments ◽  
Probability Mass Function ◽  
Traffic Lights ◽  
Traffic Conditions ◽  
Variation Distance

The automatic detection of road related information using data from sensors while driving has many potential applications such as traffic congestion detection or automatic routable map generation. This paper focuses on the automatic detection of road elements based on GPS data from on-vehicle systems. A new algorithm is developed that uses the total variation distance instead of the statistical moments to improve the classification accuracy. The algorithm is validated for detecting traffic lights, roundabouts, and street-crossings in a real scenario and the obtained accuracy (0.75) improves the best results using previous approaches based on statistical moments based features (0.71). Each road element to be detected is characterized as a vector of speeds measured when a driver goes through it. We first eliminate the speed samples in congested traffic conditions which are not comparable with clear traffic conditions and would contaminate the dataset. Then, we calculate the probability mass function for the speed (in 1 m/s intervals) at each point. The total variation distance is then used to find the similarity among different points of interest (which can contain a similar road element or a different one). Finally, a k-NN approach is used for assigning a class to each unlabelled element.

scholarly journals TOWARDS AN EFFICIENT TRAFFIC CONGESTION PREDICTION METHOD BASED ON NEURAL NETWORKS AND BIG GPS DATA

2019 ◽  
Vol 20 (1) ◽  
pp. 108-118 ◽  
Author(s):  
Wiam Elleuch ◽  
Ali Wali ◽  
Adel M. Alimi
Keyword(s):  
Traffic Congestion ◽  
Prediction Method ◽  
Traffic Information ◽  
Gps Data ◽  
Traffic Patterns ◽  
Dynamic Changes ◽  
Traffic Conditions ◽  
Congestion Prediction ◽  
Different Types ◽  
Nonparametric Techniques

ABSTRACT: The prediction of accurate traffic information such as speed, travel time, and congestion state is a very important task in many Intelligent Transportations Systems (ITS) applications. However, the dynamic changes in traffic conditions make this task harder. In fact, the type of road, such as the freeways and the highways in urban regions, can influence the driving speeds and the congestion state of the corresponding road. In this paper, we present a NNs-based model to predict the congestion state in roads. Our model handles new inputs and distinguishes the dynamic traffic patterns in two different types of roads: highways and freeways. The model has been tested using a big GPS database gathered from vehicles circulating in Tunisia. The NNs-based model has shown their capabilities of detecting the nonlinearity of dynamic changes and different patterns of roads compared to other nonparametric techniques from the literature. ABSTRAK: Ramalan maklumat trafik yang tepat seperti kelajuan, masa perjalanan dan keadaan kesesakan adalah tugas yang sangat penting dalam banyak aplikasi Sistem Pengangkutan Pintar (ITS). Walau bagaimanapun, perubahan keadaan lalu lintas yang dinamik menjadikan tugas ini menjadi lebih sukar. Malah, jenis jalan raya, seperti jalan raya dan lebuh raya di kawasan bandar, boleh mempengaruhi kelajuan memandu dan keadaan kesesakan jalan yang sama. Dalam makalah ini, kami membentangkan model berasaskan NN untuk meramalkan keadaan kesesakan di jalan raya. Model kami mengendalikan input baru dan membezakan corak trafik dinamik dalam dua jenis jalan raya yang lebuh raya dan jalan raya. Model ini telah diuji menggunakan pangkalan data GPS yang besar yang dikumpulkan dari kenderaan yang beredar di Tunisia. Model berasaskan NNs telah menunjukkan keupayaan mereka untuk mengesan ketiadaan perubahan dinamik dan pola jalan yang berbeza berbanding dengan teknik nonparametrik yang lain dari kesusasteraan.

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 A Decentralised Approach to Intersection Traffic Management

2018 ◽  
Author(s):  
Huan Vu ◽  
Samir Aknine ◽  
Sarvapali D. Ramchurn
Keyword(s):  
Quality Of Life ◽  
Waiting Time ◽  
Traffic Congestion ◽  
Traffic Management ◽  
State Of The Art ◽  
The State ◽  
Management Mechanism ◽  
Traffic Conditions

Traffic congestion has a significant impact on quality of life and the economy. This paper presents a decentralised traffic management mechanism for intersections using a distributed constraint optimisation approach (DCOP). Our solution outperforms the state of the art solution both for stable traffic conditions (about 60% reduced waiting time) and robustness to unpredictable events. 

scholarly journals Suburban Employment Centers: Probing the Influence of Site Features on the Journey-to-Work

1989 ◽  
Vol 8 (2) ◽  
pp. 75-85 ◽  
Author(s):  
Robert Cervero
Keyword(s):  
Land Use ◽  
Travel Behavior ◽  
Traffic Congestion ◽  
Low Density ◽  
Traffic Conditions ◽  
Employment Centers ◽  
Single Use ◽  
Journey To Work ◽  
Levels Of Service ◽  
The U.S

This paper argues that the low-density, single-use character of most suburban workplaces in the U.S. has contributed to worsening traffic congestion by making most workers highly dependent on their own automobiles for accessing jobs. To test this proposition, land use and transportation data are examined for fifty of the largest suburban employment centers in the nation. Differences in the share of trips made by various modes, commuting speeds, and levels of service on major thoroughfares connecting suburban centers are compared among clusters of centers. The densities, sizes, and land use mixtures of suburban workplaces are generally found to be important determinants of worker travel behavior and local traffic conditions.

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