Utilizing Artificial Neural Network in GPS-Equipped Probe Vehicles Data- Based Travel Time Estimation

The purposes of this study were to investigate accuracy and reliability of travel time information and to assess the effectiveness of probe vehicle in providing traffic information (travel time) under various traffic conditions. A widely used traffic microsimulation model, Paramics V5, was used to generate travel time data on a hypothetical network that was vuilt according to geometrics of corridor, traffic flow parameters and configuration of signalized intersection. Average travel time on links and routes were analyzed for various percentage of probe vehicles (market penetration) and traffic flow conditions (level of congestion) and compared to the ‘true’ average travel time. Moreover, the variation of travel time data obtained from probe vehicles was examined from ‘bootstrapping’ technique where this method of resampling represents the varying condition for probe vehicles. It was found that the plots of probe vehicle average travel time versus ‘tree’ average travel time for link and route case agreed well which imply the obtainment of travel time accuracy by probe vehicles in wide range of traffic volumes (situations). However, the probe vehicle performed less accurately when traffic congestion was forming. A case study of network and traffic condition similar to a real world condition was carried out to demonstrate the quality of travel time data from probe vehicles in selected routes (OD). It was clear that when having all vehicles in the link to be probes, the OD estimates are rather rlilable.

According to Vision Indonesia, data on people with eye diseases in Indonesia in 2018-2019 were 3 million people or about 1.5% of the total population. So far, public information or knowledge about the recognition of eye disease disorders is still lacking. The problem in this study is how to educate the public about the introduction of eye diseases based on information on symptoms of the disease and how to apply the web-based Artificial Neural Network (ANN) algorithm for the introduction of eye diseases. The ANN algorithm in the eye disease recognition education system can conclude knowledge even though it does not have certainty and takes it into account sequentially so that the process is faster. In terms of educational content about eye disease recognition, this is a novelty to use. This research aims to create an educational system for introducing eye diseases based on information on symptoms of the disease and applying a web-based Artificial Neural Network (ANN) algorithm for the recognition of eye diseases. The method used is the Artificial Neural Network algorithm method. The work of ANN in the education system for the introduction of eye diseases is to make parameters of eye disease symptoms or indicators that will produce the type of eye disease. The research material used is data on types of eye diseases and symptoms of each type of eye disease. The research results are to create an education system that can help the public recognise eye diseases based on the symptoms of these eye diseases that can be run on a web platform. The Artificial Neural Network (ANN) algorithm can manage input analysis data from disease indicators and show the initial results of eye diseases that can be detected. suffered by someone based on Training Results Weights and Bias v11= 1.6769, v01= 0.4356, w11= -1.5233, w01= 0.3242. Based on white box testing, the test results are free from logical errors. The results of this study indicate that the use of the ANN algorithm for eye disease recognition shows accurate results based on eye disease symptom data.

Travel time estimation (TTE) is widely applied for ride dispatching, ride-hailing, and route navigation. There are many factors affecting the travel time of a driver on a given trajectory, including the distance, road type, driving habits, traffic congestion, etc. Existing works fail to model the complex relationships of these factors for TTE. To fill this gap, in this paper, we first analyze how these factors work together in determining the travel time. In particular, the travel time depends on the distance and driving speed on each road segment of the trajectory, where the driving speed depends on the driving habits and the environment, including the static factors like the road type (highway or byway) and speed limit and the dynamic factor like the time of the day and congestion. Among these factors, driving habits and traffic conditions (e.g., jam) are the most difficult ones to model. Second, we propose to learn the driving habits of each driver via meta-learning and estimate the conditions based on the current and historical traffic conditions (via recurrent neural networks) of this road and its connected road segments (via graph convolutional neural network). The experimental results on two real taxi trajectory datasets show that our approach outperforms three state-of-the-art methods significantly.

Real-time prediction of bus travel speeds using traffic shockwaves and machine learning algorithms

Travel time is an important parameter in evaluating the operating efficiency of traffic networks, in assessing the performance of traffic management strategies, and as input to many intelligent transportation systems applications such as advanced traveler information systems. Travel time can be obtained directly from instrumented test vehicles, license plate matching, probe vehicles etc., or from indirect methods such as inductance loop detectors. Because of the widespread deployment of loop detectors, they are one of the most widely used inputs to travel time estimation techniques. There are different methods available to calculate the travel time from loop detector data, such as extrapolation of the point speed values, statistical methods, and models based on traffic flow theory. However, most of these methods fail during the transition period between the normal and congested flow conditions. The present study proposes several modifications to an existing traffic flow theory based model for travel time estimation on freeways, such that the model can estimate travel time for varying traffic flow conditions, including transition period, directly from the loop detector data. Field data collected from the I-35 freeway in San Antonio, Tex., USA, are used for illustrating the results. Automatic vehicle identification data collected from the same location are used for validating the results. Simulated data using CORSIM simulation software are also used for the validation of the model.

On urban arterials, travel time estimation is challenging especially from various data sources. Typically, fusing loop detector data and probe vehicle data to estimate travel time is a troublesome issue while considering the data issue of uncertain, imprecise and even conflicting. In this paper, we propose an improved data fusing methodology for link travel time estimation. Link travel times are simultaneously pre-estimated using loop detector data and probe vehicle data, based on which Bayesian fusion is then applied to fuse the estimated travel times. Next, Iterative Bayesian estimation is proposed to improve Bayesian fusion by incorporating two strategies: 1) substitution strategy which replaces the lower accurate travel time estimation from one sensor with the current fused travel time; and 2) specially-designed conditions for convergence which restrict the estimated travel time in a reasonable range. The estimation results show that, the proposed method outperforms probe vehicle data based method, loop detector based method and single Bayesian fusion, and the mean absolute percentage error is reduced to 4.8%. Additionally, iterative Bayesian estimation performs better for lighter traffic flows when the variability of travel time is practically higher than other periods.

The objective of this paper is to evaluate travel time estimation techniques for off-line and on-line applications using traffic speed. The study area was the 8.1-km Chalerm Mahanakorn Expressway Daokanong - Port section in Bangkok. Traffic speeds were collected at 7 stations. Average traffic volume of 6,140 vph (vehicles per hour) was observed during the 6:00am to 22:00pm period. Four offline methods were used to convert speed into travel time for each link, namely: mid-point, section average, weighted section average, and San Antonio's (lower speed). To aggregate link travel time into route travel time, the instantaneous and time slice methods were considered. The estimated travel times were then compared with the actual travel time observed from 1,632 vehicles. The results indicate that the accuracy depends on the time period and level of traffic congestion. Considering route travel time, the time slice method gives a slightly better accuracy than the instantaneous method. Comparing the offline (all data present) and online (short-term forecast of speed in the next time slice(s)) travel time estimation, the two methods yield similar accuracy. The findings of the research imply that the practical travel time estimation may need several methods suitable for each time period. The improved estimation can increase the accuracy over a single method, thereby decreasing the MAPE (mean absolute percentage error) from 14-20% to 11%.

The paper shows a method of estimating as accurately as possible the state of traffic on an urban street section delimited by applying a multicriterial calculation model for improving the estimation of the level of air quality pollution. The algorithm can be applied to a linear road section for which the travel time is minimal, data recorded for a number of cars stationed at the traffic light during the traffic light cycle of approximately 1 minute (constant value), over a period of 30 days, on sets of values at 30 min ranges for 24/24 h. The method develops a linear algorithm that can be adjusted depending on traffic flow status (crowded or fluidized)using the Kernel normalization function, which can combine both crowded and fluidized traffic (free car flow). The algorithm facilitates the tracking and recording of data from multiple sources to simulate a more accurate local traffic situation, which in turn allows for better estimates of travel time and vehicle emissions on the linear section delimitate of urban street. For optimization of the estimation results, the calculation includes the parameters of the momentary traffic status. Thus, the determination of traffic flow status leads to the setting of speed regime, accuracy of emissions estimation and fuel consumption. These aspects are support in taking operational management decisions to reduce traffic congestion and the negative impact on the environment.

Predicting truck (heavy vehicle) travel time is a principal component of freight project prioritization and planning. However, most existing travel time prediction models are designed for passenger vehicles and fail to make truck specific forecasts or use truck specific data. Little is known about the impact of this limitation, or how truck travel time prediction could be improved in response to freight investments with an improved methodology. In light of this, this paper proposes a pragmatic multi-regime speed-density relationship based approach to predict freeway truck travel time using empirical truck probe GPS data (which is increasingly available in North American and Europe) and loop detector data. Traffic regimes are segmented using a cluster analysis approach. Two case studies are presented to illustrate the approach. The travel time estimates are compared with the Bureau of Public Roads (BPR) model and the Akçelik model outputs. It is found that the proposed method is able to estimate more accurate travel times than traditional methods. The predicted travel time can support freight prioritization and planning.

Travel time is considered more useful to users than other travel-related information such as speed. It is mainly estimated by point or interval detection systems. In this study, the authors investigate the deficiency of these systems in estimating travel times when they are used in isolation, and proposed a fusion algorithm that simultaneously utilises data from both point and interval detection systems. The fusion algorithm is based on the traffic flow and k-nearest neighbourhood (k-NN) models. Specifically, the authors precisely define the so-called the time lag issue in interval detection systems. To overcome this problem, they analysed the travel time variation because of variation in traffic states using fused data from point and interval detection systems. The authors show that the travel time obtained from interval detection systems is renewed by considering the travel time variation and their results show that the proposed algorithm satisfactorily predicts the travel time with the mean absolute percentage errors (MAPE).

Forecasting Freeway Link Travel Times with a Multilayer Feedforward Neural Network

270 Background: Cancer creates a tremendous financial burden. Cancer-related costs are categorized into direct, indirect and psychosocial costs. Although there have been many reports on medical care costs, which are direct, those on other costs are extremely scarce. We estimated travel time and costs required for cancer patients to receive outpatient treatment. Methods: We studied 524 cancer patients given anti-cancer treatment between February 2009 and December 2012 at the Outpatient Chemotherapy Center of Teikyo University Chiba Medical Center. Address data were extracted from Data Warehouse electronic medical records, and travel distance and time required for outpatient treatment were calculated via MapInfo and ACT Distance Calculator Package. Transportation costs were estimated on the basis of ¥274 (= $3.00) per kilometer. Results: Average round-trip travel distances, times, and costs by cancer type are shown below. Average round-trip travel distance, time, and cost for all patients were 26.7 km, 72.5 minutes, and ¥7,303 ($79.99), respectively. Conclusions: Cancer patients incurred a travel cost of approximately ¥4,000 to ¥9,000 ($40.00 to $100.00) for each outpatient treatment. With population aging, seniors living alone and senior households are increasing, and outpatient visits are becoming a common burden. [Table: see text]

Route travel time variability estimates provide performance information related to the reliability of a trip and allow for confidence bands to be placed around the mean travel time estimates. The naïve (and sometimes used) method to estimate route travel time variance (reliability) is to assume independence of link travel times and consequently sum the individual link variances along the route. In this approach, correlation between links is assumed as zero and the approach is straightforward, but assuming independence is not realistic. This paper describes a post-processing procedure for providing improved route travel time mean and variance estimates, while taking into consideration the correlation existent between individual link travel times. Using automatic vehicle identification (AVI) and inductance loop detector (ILD) data from two separate routes in Texas, a practical application of the theory established by Fu and Rilett (1998. Expected shortest paths in dynamic and stochastic traffic networks. Transportation Research Part B: Methodological, 32(7), 499–516) is used to quantify link travel time correlation. The paper also provides an investigation on the usefulness of the loess statistical method and a polynomial regression model to estimate the distributional properties of link and route travel times. Another significant finding is that route reliability estimated from link ILD speeds (extrapolated to travel times) was not correlated to actual route travel time reliability measured by simultaneously operated probe vehicles. This work is unique in that instrumented probe vehicles were operated at exactly the same times as the roadway sensor data were collected, allowing direct comparison of the travel time estimates from all empirical data sources. The research presents valuable insight on how confidence intervals may be placed on travel time mean estimates for all traffic conditions. With the increased use of travel time data sources such as smartphones, connected vehicles, and private-sector data sources, the methods presented in this paper are invaluable for effective transportation system performance monitoring of both persons and freight movement.

Travel time is an important characteristic of traffic conditions in a road network. Up-to-date travel time information is important in dynamic traffic management. Presented are the findings of a recently completed research and evaluation program called DACCORD, regarding the evaluation of tools for online estimation and prediction of travel times by using induction loop detector data. Many methods exist with which to estimate and predict travel time by using induction loop data. Several of these methods were implemented and evaluated in three test sites in France, Italy, and the Netherlands. Both cross-tool and cross-site evaluations have been carried out. Travel time estimators based on induction loop detectors were evaluated against observed travel times and were seen to be reasonably accurate (10 percent to 15 percent root mean square error proportional) across different sites for uncongested to lightly congested traffic conditions. The evaluation period varied by site from 4 to 30 days. Results were seen to diverge at higher congestion levels: at one test site, congestion levels were seen to have a strong negative impact on estimation accuracy; at another test site, accuracy was maintained even in congested conditions.

Traffic congestion and travel time prediction based on historical congestion maps and identification of consensual days