scholarly journals ANALYSIS OF SPATIO-TEMPORAL TRAFFIC PATTERNS BASED ON PEDESTRIAN TRAJECTORIES

2016 ◽  
Vol XLI-B2 ◽  
pp. 497-503
Author(s):  
S. Busch ◽  
T. Schindler ◽  
T. Klinger ◽  
C. Brenner
Keyword(s):  
Public Transportation ◽  
Autonomous Driving ◽  
Video Data ◽  
Observation Data ◽  
Traffic Patterns ◽  
Traffic Light ◽  
The Road ◽  
Embedded Graph ◽  
Observed Behaviour ◽  
Spatio Temporal

For driver assistance and autonomous driving systems, it is essential to predict the behaviour of other traffic participants. Usually, standard filter approaches are used to this end, however, in many cases, these are not sufficient. For example, pedestrians are able to change their speed or direction instantly. Also, there may be not enough observation data to determine the state of an object reliably, e.g. in case of occlusions. In those cases, it is very useful if a prior model exists, which suggests certain outcomes. For example, it is useful to know that pedestrians are usually crossing the road at a certain location and at certain times. This information can then be stored in a map which then can be used as a prior in scene analysis, or in practical terms to reduce the speed of a vehicle in advance in order to minimize critical situations. In this paper, we present an approach to derive such a spatio-temporal map automatically from the observed behaviour of traffic participants in everyday traffic situations. In our experiments, we use one stationary camera to observe a complex junction, where cars, public transportation and pedestrians interact. We concentrate on the pedestrians trajectories to map traffic patterns. In the first step, we extract trajectory segments from the video data. These segments are then clustered in order to derive a spatial model of the scene, in terms of a spatially embedded graph. In the second step, we analyse the temporal patterns of pedestrian movement on this graph. We are able to derive traffic light sequences as well as the timetables of nearby public transportation. To evaluate our approach, we used a 4 hour video sequence. We show that we are able to derive traffic light sequences as well as time tables of nearby public transportation.

scholarly journals ANALYSIS OF SPATIO-TEMPORAL TRAFFIC PATTERNS BASED ON PEDESTRIAN TRAJECTORIES

2016 ◽  
Vol XLI-B2 ◽  
pp. 497-503 ◽  
Author(s):  
S. Busch ◽  
T. Schindler ◽  
T. Klinger ◽  
C. Brenner
Keyword(s):  
Public Transportation ◽  
Autonomous Driving ◽  
Video Data ◽  
Observation Data ◽  
Traffic Patterns ◽  
Traffic Light ◽  
The Road ◽  
Embedded Graph ◽  
Observed Behaviour ◽  
Spatio Temporal

For driver assistance and autonomous driving systems, it is essential to predict the behaviour of other traffic participants. Usually, standard filter approaches are used to this end, however, in many cases, these are not sufficient. For example, pedestrians are able to change their speed or direction instantly. Also, there may be not enough observation data to determine the state of an object reliably, e.g. in case of occlusions. In those cases, it is very useful if a prior model exists, which suggests certain outcomes. For example, it is useful to know that pedestrians are usually crossing the road at a certain location and at certain times. This information can then be stored in a map which then can be used as a prior in scene analysis, or in practical terms to reduce the speed of a vehicle in advance in order to minimize critical situations. In this paper, we present an approach to derive such a spatio-temporal map automatically from the observed behaviour of traffic participants in everyday traffic situations. In our experiments, we use one stationary camera to observe a complex junction, where cars, public transportation and pedestrians interact. We concentrate on the pedestrians trajectories to map traffic patterns. In the first step, we extract trajectory segments from the video data. These segments are then clustered in order to derive a spatial model of the scene, in terms of a spatially embedded graph. In the second step, we analyse the temporal patterns of pedestrian movement on this graph. We are able to derive traffic light sequences as well as the timetables of nearby public transportation. To evaluate our approach, we used a 4 hour video sequence. We show that we are able to derive traffic light sequences as well as time tables of nearby public transportation.

scholarly journals Mining Topological Dependencies of Recurrent Congestion in Road Networks

2021 ◽  
Vol 10 (4) ◽  
pp. 248
Author(s):  
Nicolas Tempelmeier ◽  
Udo Feuerhake ◽  
Oskar Wage ◽  
Elena Demidova
Keyword(s):  
Network Topology ◽  
Real World ◽  
Public Transportation ◽  
Road Network ◽  
Road Networks ◽  
Traffic Load ◽  
Data Mining Algorithm ◽  
Urban Road ◽  
The Road ◽  
Spatio Temporal

The discovery of spatio-temporal dependencies within urban road networks that cause Recurrent Congestion (RC) patterns is crucial for numerous real-world applications, including urban planning and the scheduling of public transportation services. While most existing studies investigate temporal patterns of RC phenomena, the influence of the road network topology on RC is often overlooked. This article proposes the ST-Discovery algorithm, a novel unsupervised spatio-temporal data mining algorithm that facilitates effective data-driven discovery of RC dependencies induced by the road network topology using real-world traffic data. We factor out regularly reoccurring traffic phenomena, such as rush hours, mainly induced by the daytime, by modelling and systematically exploiting temporal traffic load outliers. We present an algorithm that first constructs connected subgraphs of the road network based on the traffic speed outliers. Second, the algorithm identifies pairs of subgraphs that indicate spatio-temporal correlations in their traffic load behaviour to identify topological dependencies within the road network. Finally, we rank the identified subgraph pairs based on the dependency score determined by our algorithm. Our experimental results demonstrate that ST-Discovery can effectively reveal topological dependencies in urban road networks.

scholarly journals MODEL PILIHAN TRANSJAKARTA DENGAN PERBAIKAN WAKTU TEMPUH PENGGUNA JALAN GAJAH MADA DENGAN PENERAPAN ERP

2020 ◽  
Vol 3 (3) ◽  
pp. 671
Author(s):  
Rini Martini ◽  
Najid Najid
Keyword(s):  
Public Transportation ◽  
Road Pricing ◽  
Traffic Density ◽  
Observation Data ◽  
Questionnaire Data ◽  
Heavy Vehicles ◽  
Round Trip ◽  
The Public ◽  
The Road ◽  
The Relationship

Transjakarta is one of the public transportation facility provided by Indonesian government to decrease of the traffic in Jakarta, but when this government is planning to implement new traffic rules ERP (Electronic Road Pricing) or streed paid. In this study, will be discussed about the condition on jl. Gajah Mada, segment road is an access to the road towards the center of the shopping and office. To analyze the volume of vehicles will be used method of observation directly to obtain the volume, speed and traffic density. Direct observation is done by paying attention to private vehicles and heavy vehicles. With the observation data will get a graph of the relationship between speed and density that will be modified eith questionnaire data. Questionnaire data is distributed to get the percentage of Transjakarta with feeder total round trip cost with travel time. In this study it is expected to know the percentage of private vehicle users who will switch to Transjakarta with their feeders after the ERP (Electronic Road Pricing) implemented on the Gajah Mada street.  ABSTRAKTransjakarta yakni salah satu sarana transportasi publik yang diberikan pemerintah Indonesia untuk mengatasi kepadatan lalu lintas di Jakarta, namun saat ini pemerintah berencana untuk menerapkan aturan lalu lintas baru yaitu ERP (Electronic Road Pricing) atau jalan berbayar. Pada penelitian ini akan dibahas mengenai kondisi lalu lintas Jl. Gajah Mada, ruas jalan ini merupakan akses pengguna jalan untuk menuju pusat perbelanjaan dan perkantoran. Untuk menganalisa volume kendaraan akan digunakan metode observasi langsung untuk mendapatkan volume, kecepatan dan kepadatan lalu lintas. Observasi langsung dilakukan dengan memperhatikan kendaraan bermotor roda empat dan dua serta kendaraan berat. Dengan data observasi akan didapat grafik hubungan antara kecepatan dan kepadatan yang akan dimodifikasi dengan data kuesioner. Data kuesioner disebar untuk mendapatkan presentase perbaikan total biaya pulang-pergi dengan waktu tempuh bus Transjakarta dengan feedernya. Pada penelitian ini diharapkan dapat mengetahui presentase pengguna kendaraan pribadi yang akan beralih ke Transjakarta dengan feedernya setelah diberlakukannya ERP (Electronic Road Pricing) di ruas Jalan Gajah Mada.

Imaging through Scattering Media with a Learning Based Prior

2020 ◽  
Vol 2020 (14) ◽  
pp. 306-1-306-6
Author(s):  
Florian Schiffers ◽  
Lionel Fiske ◽  
Pablo Ruiz ◽  
Aggelos K. Katsaggelos ◽  
Oliver Cossairt
Keyword(s):  
Optimization Problem ◽  
Autonomous Driving ◽  
Scattering Media ◽  
Photon Scattering ◽  
Point Spread ◽  
Spread Function ◽  
Radiative Transport Equation ◽  
Spatio Temporal ◽  
Transient Imaging

Imaging through scattering media finds applications in diverse fields from biomedicine to autonomous driving. However, interpreting the resulting images is difficult due to blur caused by the scattering of photons within the medium. Transient information, captured with fast temporal sensors, can be used to significantly improve the quality of images acquired in scattering conditions. Photon scattering, within a highly scattering media, is well modeled by the diffusion approximation of the Radiative Transport Equation (RTE). Its solution is easily derived which can be interpreted as a Spatio-Temporal Point Spread Function (STPSF). In this paper, we first discuss the properties of the ST-PSF and subsequently use this knowledge to simulate transient imaging through highly scattering media. We then propose a framework to invert the forward model, which assumes Poisson noise, to recover a noise-free, unblurred image by solving an optimization problem.

CREATING WALKING LED TRANSIT STATIONS FOR WOMEN IN MALAYSIA

2020 ◽  
Vol 7 (1) ◽  
pp. 99
Author(s):  
Yong Adilah Shamsul Harumain ◽  
Nur Farhana Azmi ◽  
Suhaini Yusoff
Keyword(s):  
Quality Of Life ◽  
Developing Countries ◽  
Public Transportation ◽  
Carbon Emission ◽  
The Other ◽  
Walking Distance ◽  
The Road ◽  
On The Road ◽  
Female User

Transit stations are generally well known as nodes of spaces where percentage of people walking are relatively high. The issue is do more planning is actually given to create walkability. Creating walking led transit stations involves planning of walking distance, providing facilities like pathways, toilets, seating and lighting. On the other hand, creating walking led transit station for women uncover a new epitome. Walking becomes one of the most important forms of mobility for women in developing countries nowadays. Encouraging women to use public transportation is not just about another effort to promote the use of public transportation but also another great endeavour to reduce numbers of traffic on the road. This also means, creating an effort to control accidents rate, reducing carbon emission, improving health and eventually, developing the quality of life. Hence, in this paper, we sought first to find out the factors that motivate women to walk at transit stations in Malaysia. A questionnaire survey with 562 female user of Light Railway Transit (LRT) was conducted at LRT stations along Kelana Jaya Line. Both built and non-built environment characteristics, particularly distance, safety and facilities were found as factors that are consistently associated with women walkability. With these findings, the paper highlights the criteria  which are needed to create and make betterment of transit stations not just for women but also for walkability in general.

scholarly journals The Evaluation of Experimental Variables for Sustainable Virtual Road Safety Audits

2021 ◽  
Vol 13 (11) ◽  
pp. 5899
Author(s):  
Yeonsoo Jun ◽  
Juneyoung Park ◽  
Chunho Yeom
Keyword(s):  
Road Safety ◽  
Traffic Light ◽  
Scenario Development ◽  
Driving Simulators ◽  
Traffic Condition ◽  
The Road ◽  
Road Pavement ◽  
Net Promoter ◽  
Road Safety Audits ◽  
Road Marking

This paper evaluates experimental variables for virtual road safety audits (VRSAs) through practical experiments to promote sustainable road safety. VRSAs perform road safety audits using driving simulators (DSs), and all objects in the road environment cannot be experimental variables because of realistic constraints. Therefore, the study evaluates the likelihood of recommendation of VRSA experimental variables by comparing DSs experiments and field reviews to secure sustainable road safety conditions. The net promoter score results evaluated “Tunnel”, “Bridge”, “Underpass”, “Footbridge”, “Traffic island”, “Sign”, “Lane”, “Road marking”, “Traffic light”, “Median barrier”, “Road furniture”, and “Traffic condition” as recommended variables. On the contrary, the “Road pavement”, “Drainage”, “Lighting”, “Vehicle”, “Pedestrian”, “Bicycle”, “Accident”, and “Hazard event” variables were not recommended. The study can be used for decision making in VRSA scenario development as an initial effort to evaluate its experimental variables.

scholarly journals Automatic Roadway Features Detection with Oriented Object Detection

2021 ◽  
Vol 11 (8) ◽  
pp. 3531
Author(s):  
Hesham M. Eraqi ◽  
Karim Soliman ◽  
Dalia Said ◽  
Omar R. Elezaby ◽  
Mohamed N. Moustafa ◽  
...  
Keyword(s):  
Object Detection ◽  
Safety Evaluation ◽  
Autonomous Driving ◽  
Detection Accuracy ◽  
The Road ◽  
Detection Model ◽  
Detection Approach ◽  
Roadway Safety ◽  
Safety Features ◽  
Oriented Object

Extensive research efforts have been devoted to identify and improve roadway features that impact safety. Maintaining roadway safety features relies on costly manual operations of regular road surveying and data analysis. This paper introduces an automatic roadway safety features detection approach, which harnesses the potential of artificial intelligence (AI) computer vision to make the process more efficient and less costly. Given a front-facing camera and a global positioning system (GPS) sensor, the proposed system automatically evaluates ten roadway safety features. The system is composed of an oriented (or rotated) object detection model, which solves an orientation encoding discontinuity problem to improve detection accuracy, and a rule-based roadway safety evaluation module. To train and validate the proposed model, a fully-annotated dataset for roadway safety features extraction was collected covering 473 km of roads. The proposed method baseline results are found encouraging when compared to the state-of-the-art models. Different oriented object detection strategies are presented and discussed, and the developed model resulted in improving the mean average precision (mAP) by 16.9% when compared with the literature. The roadway safety feature average prediction accuracy is 84.39% and ranges between 91.11% and 63.12%. The introduced model can pervasively enable/disable autonomous driving (AD) based on safety features of the road; and empower connected vehicles (CV) to send and receive estimated safety features, alerting drivers about black spots or relatively less-safe segments or roads.

scholarly journals Extracting Stops from Spatio-Temporal Trajectories within Dynamic Contextual Features

2021 ◽  
Vol 13 (2) ◽  
pp. 690
Author(s):  
Tao Wu ◽  
Huiqing Shen ◽  
Jianxin Qin ◽  
Longgang Xiang
Keyword(s):  
Moving Objects ◽  
Major Effect ◽  
Location Based Services ◽  
Trajectory Data ◽  
The Road ◽  
Contextual Features ◽  
Novel Approach ◽  
Gps Trajectories ◽  
Time Distance ◽  
Spatio Temporal

Identifying stops from GPS trajectories is one of the main concerns in the study of moving objects and has a major effect on a wide variety of location-based services and applications. Although the spatial and non-spatial characteristics of trajectories have been widely investigated for the identification of stops, few studies have concentrated on the impacts of the contextual features, which are also connected to the road network and nearby Points of Interest (POIs). In order to obtain more precise stop information from moving objects, this paper proposes and implements a novel approach that represents a spatio-temproal dynamics relationship between stopping behaviors and geospatial elements to detect stops. The relationship between the candidate stops based on the standard time–distance threshold approach and the surrounding environmental elements are integrated in a complex way (the mobility context cube) to extract stop features and precisely derive stops using the classifier classification. The methodology presented is designed to reduce the error rate of detection of stops in the work of trajectory data mining. It turns out that 26 features can contribute to recognizing stop behaviors from trajectory data. Additionally, experiments on a real-world trajectory dataset further demonstrate the effectiveness of the proposed approach in improving the accuracy of identifying stops from trajectories.

scholarly journals Simulating Spatio-Temporal Patterns of Bicycle Flows with an Agent-Based Model

2021 ◽  
Vol 10 (2) ◽  
pp. 88
Author(s):  
Dana Kaziyeva ◽  
Martin Loidl ◽  
Gudrun Wallentin
Keyword(s):  
Regional Scale ◽  
Evidence Base ◽  
Wide Field ◽  
Traffic Patterns ◽  
Agent Based Model ◽  
Validation Data ◽  
Agent Based ◽  
Mobility Behavior ◽  
Spatio Temporal ◽  
Models Validation

Transport planning strategies regard cycling promotion as a suitable means for tackling problems connected with motorized traffic such as limited space, congestion, and pollution. However, the evidence base for optimizing cycling promotion is weak in most cases, and information on bicycle patterns at a sufficient resolution is largely lacking. In this paper, we propose agent-based modeling to simulate bicycle traffic flows at a regional scale level for an entire day. The feasibility of the model is demonstrated in a use case in the Salzburg region, Austria. The simulation results in distinct spatio-temporal bicycle traffic patterns at high spatial (road segments) and temporal (minute) resolution. Scenario analysis positively assesses the model’s level of complexity, where the demographically parametrized behavior of cyclists outperforms stochastic null models. Validation with reference data from three sources shows a high correlation between simulated and observed bicycle traffic, where the predictive power is primarily related to the quality of the input and validation data. In conclusion, the implemented agent-based model successfully simulates bicycle patterns of 186,000 inhabitants within a reasonable time. This spatially explicit approach of modeling individual mobility behavior opens new opportunities for evidence-based planning and decision making in the wide field of cycling promotion

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