Opportunities and challenges of smart mobile applications in transportation

Siuhi, Saidi; Mwakalonge, Judith

doi:10.1016/j.jtte.2016.11.001

Cited by 77 publications

(51 citation statements)

References 4 publications

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“…Mobile phone navigation devices have several advantages over the former methods such as high accuracy, reliability, optimal performance in real-time, less construction time and costs, etc. [30]. To detect the instantaneous speed of vehicles, remote traffic microwave sensor (RTMS) is another useful non-intrusive new piece of equipment.…”

Section: Literature Reviewmentioning

confidence: 99%

Freeway Short-Term Travel Speed Prediction Based on Data Collection Time-Horizons: A Fast Forest Quantile Regression Approach

et al. 2020

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Short-term traffic speed prediction is vital for proactive traffic control, and is one of the integral components of an intelligent transportation system (ITS). Accurate prediction of short-term travel speed has numerous applications for traffic monitoring, route planning, as well as helping to relieve traffic congestion. Previous studies have attempted to approach this problem using statistical and conventional artificial intelligence (AI) methods without accounting for influence of data collection time-horizons. However, statistical methods have received widespread criticism concerning prediction accuracy performance, while traditional AI approaches have too shallow architecture to capture non-linear stochastics variations in traffic flow. Hence, this study aims to explore prediction of short-term traffic speed at multiple time-ahead intervals using data collected from loop detectors. A fast forest quantile regression (FFQR) via hyperparameters optimization was introduced for predicting short-term traffic speed prediction. FFQR is an ensemble machine learning model that combines several regression trees to improve speed prediction accuracy. The accuracy of short-term traffic speed prediction was compared using the FFQR model at different data collection time-horizons. Prediction results demonstrated the adequacy and robustness of the proposed approach under different scenarios. It was concluded that prediction performance of FFQR was significantly enhanced and robust, particularly at time intervals larger than 5 min. The findings also revealed that speed prediction error (in terms of quantiles loss) ranged between 0.58 and 1.18.

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Section: Literature Reviewmentioning

confidence: 99%

Freeway Short-Term Travel Speed Prediction Based on Data Collection Time-Horizons: A Fast Forest Quantile Regression Approach

et al. 2020

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“…(Siuhi and Mwakalonge, 2016). It suggests various mobile applications which can be used instead of high cost equipment.…”

Section: Reviewmentioning

confidence: 99%

Travel Time Prediction Modelling in Mixed Traffic Conditions

2018

IJTTE

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Abstract:Over the past few decades, exponential increase in vehicle ownership resulted in issues of traffic control and management. Intelligent Transportation System (ITS) is one of the solutions for the intelligent management of traffic. ITS applications like Advanced Traveler Information Systems and Advanced Traffic Management Systems need travel time as a major input. The estimation of travel time in urban network became more complicated because of the rapid change in the system and traffic. This study is done in order to assess the impact of different travel modes on travel time. Data has been collected on a stretch of 14 km length in Warangal city, India using GPS probe vehicle along with video camera. Different private modes of transportation such as 2 wheeler, passenger car and 3 wheeler have been used as test vehicles for the collection of data in different traffic flow scenarios. Artificial Neural Network and a multi linear regression model have been developed to compare the estimated travel times with the field data. Two combinations of ANN model using single hidden layer, different numbers of neurons and epochs have been compared. The travel time of different modes has been compared and the effect of vehicle composition on travel time has been analyzed. The ANN model perform better than the regression model.

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“…As a result, inexperienced passengers often experience difficulties and eventually switch to another mode of transportation. The availability of route information is very important as one of factors that affects the quality of public transportation [2]. Research in Bandung city revealed that the most needed requirement by passenger was information on the paratransit route [3].…”

Section: Introductionmentioning

confidence: 99%

Prototype of REST Location-based Service for Trip Planning by Paratransit (Mikrolet) in Malang City

Kharisma

Pinandito

2019

JITeCS

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The number of passengers of local public transport (mikrolet) in Malang city has been receding. One of the contributing factors is the limited information about the route of mikrolet. This study aims to create a prototype that can produce information about routes of mikrolet, travel planning (places to take a mikrolet and places to take off the mikrolet), map representation of travel planning, and travel plans alternatives. To calculate travel plan and all its alternatives, a modified depth-first search algorithm is used. This study uses depth-first search algorithm because it is considered simpler and can be modified to meet the requirements. Based on test results, the prototype successfully provides all the information needed. From performance perspective, when the number of routes and plans increased, the overall system performance degrades, so in the future the proposed prototype and its algorithm may needs further optimization.

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Opportunities and challenges of smart mobile applications in transportation

Cited by 77 publications

References 4 publications

Freeway Short-Term Travel Speed Prediction Based on Data Collection Time-Horizons: A Fast Forest Quantile Regression Approach

Freeway Short-Term Travel Speed Prediction Based on Data Collection Time-Horizons: A Fast Forest Quantile Regression Approach

Travel Time Prediction Modelling in Mixed Traffic Conditions

Prototype of REST Location-based Service for Trip Planning by Paratransit (Mikrolet) in Malang City

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