Estimation of Travel Time in the City Using Neural Networks Trained with Simulated Urban Traffic Data

2018 ◽  
pp. 121-134
Author(s):  
Piotr Ciskowski ◽  
Grzegorz Drzewiński ◽  
Marek Bazan ◽  
Tomasz Janiczek
Keyword(s):  
Neural Networks ◽  
Travel Time ◽  
Urban Traffic ◽  
Traffic Data ◽  
The City

scholarly journals Integrative analysis of multimodal traffic data: addressing open challenges using big data analytics in the city of Lisbon

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Carlos Lemonde ◽  
Elisabete Arsenio ◽  
Rui Henriques
Keyword(s):  
Big Data ◽  
City Council ◽  
Urban Traffic ◽  
Integrative Analysis ◽  
Context Aware ◽  
Traffic Data ◽  
Situational Context ◽  
Transport Modes ◽  
The City ◽  
Urban Data

AbstractWorldwide cities are establishing efforts to collect urban traffic data from various modes and sources. Integrating traffic data, together with their situational context, offers more comprehensive views on the ongoing mobility changes and supports enhanced management decisions accordingly. Hence, cities are becoming sensorized and heterogeneous sources of urban data are being consolidated with the aim of monitoring multimodal traffic patterns, encompassing all major transport modes—road, railway, inland waterway—, and active transport modes such as walking and cycling. The research reported in this paper aims at bridging the existing literature gap on the integrative analysis of multimodal traffic data and its situational urban context. The reported work is anchored on the major findings and contributions from the research and innovation project Integrative Learning from Urban Data and Situational Context for City Mobility Optimization (ILU), a multi-disciplinary project on the field of artificial intelligence applied to urban mobility, joining the Lisbon city Council, public carriers, and national research institutes. The manuscript is focused on the context-aware analysis of multimodal traffic data with a focus on public transportation, offering four major contributions. First, it provides a structured view on the scientific and technical challenges and opportunities for data-centric multimodal mobility decisions. Second, rooted on existing literature and empirical evidence, we outline principles for the context-aware discovery of multimodal patterns from heterogeneous sources of urban data. Third, Lisbon is introduced as a case study to show how these principles can be enacted in practice, together with some essential findings. Finally, we instantiate some principles by conducting a spatiotemporal analysis of multimodality indices in the city against available context. Concluding, this work offers a structured view on the opportunities offered by cross-modal and context-enriched analysis of traffic data, motivating the role of Big Data to support more transparent and inclusive mobility planning decisions, promote coordination among public transport operators, and dynamically align transport supply with the emerging urban traffic dynamics.

scholarly journals Introducing a Navigation Algorithm for Reducing the Spread of Diseases in Public Transport Networks

2021 ◽  
Author(s):  
Jens Klinker ◽  
Mohamed Hechem Selmi ◽  
Mariana Avezum ◽  
Stephan Jonas
Keyword(s):  
Travel Time ◽  
Public Transport ◽  
Public Transportation ◽  
Urban Traffic ◽  
Planning Problem ◽  
Navigation Algorithm ◽  
Urban Public Transport ◽  
Flow Through ◽  
The Impact ◽  
The City

Reducing passenger flow through highly frequented bottlenecks in public transportation networks is a well-known urban planning problem. This issue has become even more relevant since the outbreak of the SARS-CoV-2 pandemic and the necessity for minimum distances between passengers. We propose an approach that allows to dynamically navigate passengers around dangerously crowded stations to better distribute the passenger load across an entire urban public transport network. This is achieved through the introduction of new constraints into routing requests, that enable the avoidance of specific nodes in a network. These requests consider walks, bikes, metros, subways, trams and buses as possible modes of transportation. An implementation of the approach is provided in cooperation with the Munich Travel Corporation (MVG) for the city of Munich, to simulate the effects on a real city’s urban traffic flow. Among other factors, the impact on the travel time was simulated given that the two major exchange points in the network were to be avoided. With an increase from 26.5 to 26.8 minutes on the average travel time, the simulation suggests that the time penalty might be worth the safety benefits.

scholarly journals UTRaLab – Urban Traffic Research Laboratory

2017 ◽  
Vol 3 ◽  
Author(s):  
Karsten Kozempel ◽  
Andreas Luber ◽  
Marek Junghans
Keyword(s):  
Data Collection ◽  
Research Laboratory ◽  
Urban Traffic ◽  
Environmental Data ◽  
Detection Methods ◽  
Traffic Data ◽  
Traffic Sensors ◽  
The One ◽  
The City ◽  
Traffic Detection

The Urban Traffic Research Laboratory (UTRaLab) is a research and test track for traffic detection methods and sensors. It is located at the Ernst-Ruska-Ufer, in the southeast of the city of Berlin (Germany). The UTRaLab covers 1 km of a highly-frequented urban road and is connected to a motorway. It is equipped with two gantries with distance of 850 m in between and has several outstations for data collection. The gantries contain many different traffic sensors like inductive loops, cameras, lasers or wireless sensors for traffic data acquisition. Additionally a weather station records environmental data. The UTRaLab’s main purposes are the data collection of traffic data on the one hand and testing newly developed sensors on the other hand.

Short-term forecast model of taxi demand based on time and space heterogeneity

2021 ◽  
pp. 1-12
Author(s):  
Zhiyu Yan ◽  
Shuang Lv
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Urban Traffic ◽  
Percentage Error ◽  
Density Level ◽  
Traffic Data ◽  
Short Term ◽  
Time And Space ◽  
Density Data

Accurate prediction of traffic flow is of great significance for alleviating urban traffic congestions. Most previous studies used historical traffic data, in which only one model or algorithm was adopted by the whole prediction space and the differences in various regions were ignored. In this context, based on time and space heterogeneity, a Classification and Regression Trees-K-Nearest Neighbor (CART-KNN) Hybrid Prediction model was proposed to predict short-term taxi demand. Firstly, a concentric partitioning method was applied to divide the test area into discrete small areas according to its boarding density level. Then the CART model was used to divide the dataset of each area according to its temporal characteristics, and KNN was established for each subset by using the corresponding boarding density data to estimate the parameters of the KNN model. Finally, the proposed method was tested on the New York City Taxi and Limousine Commission (TLC) data, and the traditional KNN model, backpropagation (BP) neural network, long-short term memory model (LSTM) were used to compare with the proposed CART-KNN model. The selected models were used to predict the demand for taxis in New York City, and the Kriging Interpolation was used to obtain all the regional predictions. From the results, it can be suggested that the proposed CART-KNN model performed better than other general models by showing smaller mean absolute percentage error (MAPE) and root mean square error (RMSE) value. The improvement of prediction accuracy of CART-KNN model is helpful to understand the regional demand pattern to partition the boarding density data from the time and space dimensions. The partition method can be extended into many models using traffic data.

scholarly journals Measurements of urban traffic parameters before and after road reconstruction

2021 ◽  
Vol 11 (1) ◽  
pp. 365-376
Author(s):  
Andrzej Bąkowski ◽  
Leszek Radziszewski
Keyword(s):  
Urban Traffic ◽  
Noise Model ◽  
Vehicle Traffic ◽  
The Road ◽  
Passenger Vehicles ◽  
Before And After ◽  
Traffic Volumes ◽  
National Road ◽  
The City ◽  
Good Agreement

Abstract The study analyzed the parameters of vehicle traffic and noise on the national road in the section in the city from 2011 to 2016. In 2013–2014 this road was reconstructed. It was found that in most cases, the distribution of the tested variable was not normal. The median and selected percentiles of vehicle traffic parameters and noise were examined. The variability and type A uncertainty of the results were described and evaluated. The results obtained for the data recorded on working and non-working days were compared. The vehicle cumulative speed distributions, for two-way four-lane road segments in both directions were analyzed. A mathematical model of normalized traffic flow has been proposed. Fit factor R2 of the proposed equations to the experimental data for passenger vehicles ranges from 0.93 to 0.99. It has been shown that two years after the road reconstruction, the median noise level did not increase even though traffic volumes and vehicle speeds increased. The Cnossos noise model was validated for data recorded over a period of 6 years. A very good agreement of the medians determined according to the Cnossos-EU model and the measured ones was obtained. It should be noted, however, that for the other analyzed percentiles, e.g. 95%, the discrepancies are larger.

TripRes

2021 ◽  
Vol 17 (2) ◽  
pp. 1-21
Author(s):  
Xiaolong Xu ◽  
Zijie Fang ◽  
Lianyong Qi ◽  
Xuyun Zhang ◽  
Qiang He ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Discrete Distribution ◽  
Resource Provisioning ◽  
Path Selection ◽  
Urban Traffic ◽  
Traffic Flows ◽  
Multimedia Services ◽  
Traffic Flow Prediction ◽  
The City ◽  
Multimedia Surveillance

The Internet of Vehicles (IoV) connects vehicles, roadside units (RSUs) and other intelligent objects, enabling data sharing among them, thereby improving the efficiency of urban traffic and safety. Currently, collections of multimedia content, generated by multimedia surveillance equipment, vehicles, and so on, are transmitted to edge servers for implementation, because edge computing is a formidable paradigm for accommodating multimedia services with low-latency resource provisioning. However, the uneven or discrete distribution of the traffic flow covered by edge servers negatively affects the service performance (e.g., overload and underload) of edge servers in multimedia IoV systems. Therefore, how to accurately schedule and dynamically reserve proper numbers of resources for multimedia services in edge servers is still challenging. To address this challenge, a traffic flow prediction driven resource reservation method, called TripRes, is developed in this article. Specifically, the city map is divided into different regions, and the edge servers in a region are treated as a “big edge server” to simplify the complex distribution of edge servers. Then, future traffic flows are predicted using the deep spatiotemporal residual network (ST-ResNet), and future traffic flows are used to estimate the amount of multimedia services each region needs to offload to the edge servers. With the number of services to be offloaded in each region, their offloading destinations are determined through latency-sensitive transmission path selection. Finally, the performance of TripRes is evaluated using real-world big data with over 100M multimedia surveillance records from RSUs in Nanjing China.

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