travel time
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2022 ◽  
Vol 27 ◽  
pp. 95-106
Eva Malichová ◽  
Yannick Cornet ◽  
Martin Hudák

2022 ◽  
Vol 13 (1) ◽  
pp. 1-18
Meng Chen ◽  
Qingjie Liu ◽  
Weiming Huang ◽  
Teng Zhang ◽  
Yixuan Zuo ◽  

Next location prediction is of great importance for many location-based applications and provides essential intelligence to various businesses. In previous studies, a common approach to next location prediction is to learn the sequential transitions with massive historical trajectories based on conditional probability. Nevertheless, due to the time and space complexity, these methods (e.g., Markov models) only utilize the just passed locations to predict next locations, neglecting earlier passed locations in the trajectory. In this work, we seek to enhance the prediction performance by incorporating the travel time from all the passed locations in the query trajectory to each candidate next location. To this end, we propose a novel prediction method, namely the Travel Time Difference Model, which exploits the difference between the shortest travel time and the actual travel time to predict next locations. Moreover, we integrate the Travel Time Difference Model with a Sequential and Temporal Predictor to yield a joint model. The joint prediction model integrates local sequential transitions, temporal regularity, and global travel time information in the trajectory for the next location prediction problem. We have conducted extensive experiments on two real-world datasets: the vehicle passage record data and the taxi trajectory data. The experimental results demonstrate significant improvements in prediction accuracy over baseline methods.

2022 ◽  
Vol 271 ◽  
pp. 52-58
J. Hunter Mehaffey ◽  
J. Michael Cullen ◽  
Robert B. Hawkins ◽  
Clifford Fonner ◽  
John Kern ◽  

2022 ◽  
Vol 167 ◽  
pp. 108594
Guang-Heng Luo ◽  
Jian-Wen Pan ◽  
Jin-Ting Wang ◽  
Feng Jin

Jing Cao ◽  
Yuchuan Du ◽  
Lu Mao ◽  
Yuxiong Ji ◽  
Fei Ma ◽  

Camila P. Cagna ◽  
Osvaldo Guedes Filho ◽  
Alexandre R. C. Silva ◽  
Cássio A. Tormena

ABSTRACT The objective of this study was to automate the acquisition of water travel time, as well as the computation of hydraulic conductivity of saturated soil by the falling head method, using water sensors and the Arduino platform. To automate the measurement of travel time, the Arduino Uno board was used, and two water sensors were installed at the initial (h0) and final (h1) heights of the water inside the core. When the water flows across the soil and the water level passes the bottom part of the initial sensor (h0), the time recording starts; it ends when the water is absent from the final height of the second sensor (h1). The equation for calculating the hydraulic conductivity was inserted into the algorithm so the calculation was automatic. Undisturbed soil samples were taken in a long-term no-tillage area. There were no significant differences for the time and hydraulic conductivity means between the permeameters. The coefficient of the residual mass index showed an overestimation of the time variable; thus, the automated permeameter improves the precision of time recording and saturated hydraulic conductivity estimated by the falling head method.

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262496
Oded Cats ◽  
Rafal Kucharski ◽  
Santosh Rao Danda ◽  
Menno Yap

Since ride-hailing has become an important travel alternative in many cities worldwide, a fervent debate is underway on whether it competes with or complements public transport services. We use Uber trip data in six cities in the United States and Europe to identify the most attractive public transport alternative for each ride. We then address the following questions: (i) How does ride-hailing travel time and cost compare to the fastest public transport alternative? (ii) What proportion of ride-hailing trips do not have a viable public transport alternative? (iii) How does ride-hailing change overall service accessibility? (iv) What is the relation between demand share and relative competition between the two alternatives? Our findings suggest that the dichotomy—competing with or complementing—is false. Though the vast majority of ride-hailing trips have a viable public transport alternative, between 20% and 40% of them have no viable public transport alternative. The increased service accessibility attributed to the inclusion of ride-hailing is greater in our US cities than in their European counterparts. Demand split is directly related to the relative competitiveness of travel times i.e. when public transport travel times are competitive ride-hailing demand share is low and vice-versa.

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262535
Xinhuan Zhang ◽  
Les Lauber ◽  
Hongjie Liu ◽  
Junqing Shi ◽  
Meili Xie ◽  

Improving travel time prediction for public transit effectively enhances service reliability, optimizes travel structure, and alleviates traffic problems. Its greater time-variance and uncertainty make predictions for short travel times (≤35min) more subject to be influenced by random factors. It requires higher precision and is more complicated than long-term predictions. Effectively extracting and mining real-time, accurate, reliable, and low-cost multi-source data such as GPS, AFC, and IC can provide data support for travel time prediction. Kalman filter model has high accuracy in one-step prediction and can be used to calculate a large amount of data. This paper adopts the Kalman filter as a travel time prediction model for a single bus based on single-line detection: including the travel time prediction model of route (RTM) and the stop dwell time prediction model (DTM); the evaluation criteria and indexes of the models are given. The error analysis of the prediction results is carried out based on AVL data by case study. Results show that under the precondition of multi-source data, the public transportation prediction model can meet the accuracy requirement for travel time prediction and the prediction effect of the whole route is superior to that of the route segment between stops.

Qibin Zhou ◽  
Qingang Su ◽  
Dingyu Yang

Real-time traffic estimation focuses on predicting the travel time of one travel path, which is capable of helping drivers selecting an appropriate or favor path. Statistical analysis or neural network approaches have been explored to predict the travel time on a massive volume of traffic data. These methods need to be updated when the traffic varies frequently, which incurs tremendous overhead. We build a system RealTER⁢e⁢a⁢l⁢T⁢E, implemented on a popular and open source streaming system StormS⁢t⁢o⁢r⁢m to quickly deal with high speed trajectory data. In RealTER⁢e⁢a⁢l⁢T⁢E, we propose a locality-sensitive partition and deployment algorithm for a large road network. A histogram estimation approach is adopted to predict the traffic. This approach is general and able to be incremental updated in parallel. Extensive experiments are conducted on six real road networks and the results illustrate RealTE achieves higher throughput and lower prediction error than existing methods. The runtime of a traffic estimation is less than 11 seconds over a large road network and it takes only 619619 microseconds for model updates.

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