Dynamic traffic demand prediction using conventional and emerging data sources

2006 ◽  
Vol 153 (1) ◽  
pp. 97 ◽  
Author(s):  
C. Antoniou ◽  
M. Ben-Akiva ◽  
H.N. Koutsopoulos
Keyword(s):  
Data Sources ◽  
Dynamic Traffic ◽  
Traffic Demand ◽  
Demand Prediction

scholarly journals Optimization of Pavement Inspection Schedule with Traffic Demand Prediction

2016 ◽  
Vol 218 ◽  
pp. 95-103 ◽  
Author(s):  
Soomin Woo ◽  
Hwasoo Yeo
Keyword(s):  
Traffic Demand ◽  
Demand Prediction ◽  
Inspection Schedule

Optimal Routing for Wireless Mesh Networks With Dynamic Traffic Demand

2008 ◽  
Vol 13 (1-2) ◽  
pp. 97-116 ◽  
Author(s):  
Liang Dai ◽  
Yuan Xue ◽  
Bin Chang ◽  
Yanchuan Cao ◽  
Yi Cui
Keyword(s):  
Wireless Mesh Networks ◽  
Mesh Networks ◽  
Optimal Routing ◽  
Dynamic Traffic ◽  
Traffic Demand ◽  
Wireless Mesh

Taxi Booking Mobile App Order Demand Prediction Based on Short-Term Traffic Forecasting

2017 ◽  
Vol 2634 (1) ◽  
pp. 57-68 ◽  
Author(s):  
Yunxuan Li ◽  
Jian Lu ◽  
Lin Zhang ◽  
Yi Zhao
Keyword(s):  
High Frequency ◽  
Demand Forecasting ◽  
Low Frequency ◽  
Original Data ◽  
Mobile App ◽  
Support Vector ◽  
Short Term ◽  
Traffic Demand ◽  
Traffic Forecasting ◽  
Demand Prediction

The Didi Dache app is China’s biggest taxi booking mobile app and is popular in cities. Unsurprisingly, short-term traffic demand forecasting is critical to enabling Didi Dache to maximize use by drivers and ensure that riders can always find a car whenever and wherever they may need a ride. In this paper, a short-term traffic demand forecasting model, Wave SVM, is proposed. It combines the complementary advantages of Daubechies5 wavelets analysis and least squares support vector machine (LS-SVM) models while it overcomes their respective shortcomings. This method includes four stages: in the first stage, original data are preprocessed; in the second stage, these data are decomposed into high-frequency and low-frequency series by wavelet; in the third stage, the prediction stage, the LS-SVM method is applied to train and predict the corresponding high-frequency and low-frequency series; in the last stage, the diverse predicted sequences are reconstructed by wavelet. The real taxi-hailing orders data are applied to evaluate the model’s performance and practicality, and the results are encouraging. The Wave SVM model, compared with the prediction error of state-of-the-art models, not only has the best prediction performance but also appears to be the most capable of capturing the nonstationary characteristics of the short-term traffic dynamic systems.

Green small cell planning in smart cities under dynamic traffic demand

2015 ◽  
Author(s):  
Li Zhou ◽  
Xiping Hu ◽  
Chunsheng Zhu ◽  
Edith C.-H. Ngai ◽  
Shan Wang ◽  
...  
Keyword(s):  
Smart Cities ◽  
Small Cell ◽  
Dynamic Traffic ◽  
Traffic Demand ◽  
Cell Planning

scholarly journals Probabilistic En Route Sector Traffic Demand Prediction Based upon Statistical Analysis of Error Distribution Characteristics

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Wen Tian ◽  
Huiqing Xu ◽  
Yixing Guo ◽  
Bin Hu ◽  
Yi Yao
Keyword(s):  
Traffic Congestion ◽  
Southern China ◽  
Probabilistic Method ◽  
Prediction Method ◽  
Weather Conditions ◽  
Error Distribution ◽  
Time Interval ◽  
Distribution Characteristics ◽  
Traffic Demand ◽  
Demand Prediction

In China, air traffic congestion has become increasingly prominent and tends to spread from terminal areas to en route networks. Accurate and objective traffic demand prediction could alleviate congestion effectively. However, the usual demand prediction is based on conjecture method of flying track, and the number of aircraft flying over a sector in a set time interval could be inferred through the location information of any aircraft track. In this paper, we proposed a probabilistic traffic demand prediction method by considering the deviations caused by random events, such as the change of departure or arrival time, the temporary change in route or altitude under severe weather conditions, and unscheduled cancellation for a flight. The probabilistic method quantifies these uncertain factors and presents numerical value with its corresponding probability instead of the deterministic number of aircraft in a sector during a time interval. The analysis results indicate that the probabilistic traffic demand prediction based on error distribution characteristics achieves an effective match with the realistic operation in airspace of central and southern China, which contributes to enhancing the implementation of airspace congestion risk management.

scholarly journals A Markov Chain Based Demand Prediction Model for Stations in Bike Sharing Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Yajun Zhou ◽  
Lilei Wang ◽  
Rong Zhong ◽  
Yulong Tan
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Prediction Method ◽  
Chain Model ◽  
Daily Production ◽  
Forecasting Accuracy ◽  
Traffic Demand ◽  
Demand Prediction ◽  
Proposed Model ◽  
Bike Sharing

Accurate transfer demand prediction at bike stations is the key to develop balancing solutions to address the overutilization or underutilization problem often occurring in bike sharing system. At the same time, station transfer demand prediction is helpful to bike station layout and optimization of the number of public bikes within the station. Traditional traffic demand prediction methods, such as gravity model, cannot be easily adapted to the problem of forecasting bike station transfer demand due to the difficulty in defining impedance and distinct characteristics of bike stations (Xu et al. 2013). Therefore, this paper proposes a prediction method based on Markov chain model. The proposed model is evaluated based on field data collected from Zhongshan City bike sharing system. The daily production and attraction of stations are forecasted. The experimental results show that the model of this paper performs higher forecasting accuracy and better generalization ability.

scholarly journals Tourism Traffic Demand Prediction Using Google Trends Based on EEMD-DBN

Engineering ◽  
2020 ◽  
Vol 12 (03) ◽  
pp. 194-215
Author(s):  
Yi Xiao ◽  
Xueting Tian ◽  
Ming Xiao
Keyword(s):  
Google Trends ◽  
Traffic Demand ◽  
Demand Prediction
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