10 Having access to accurate travel time is of great importance for both highway networ k users and 11 traffic engineers . The trave l time which is currently reported on several highways is estimated by 12 employing naïve methods and using limited sources of data. This results in unreliable and 13 inaccurate travel time prediction and could impose delay on travelers. Therefore, t he main 14 objective of this study is short-term predicti on of travel time for highways using multiple data 15 sources including loop detectors, probe vehicles, weather condition, network, accident s, road 16 works, and special events in order to consider the effect of differen t factors on travel time . To this 17 end, two machine learning methods , K-Nearest Neighbors and Random Forest , are employed . 18 After applying data cleaning process on datasets and combining them, the models are trained to 19 predict and compare short -term harmonic average speed as a representative of travel time for 5 - 20 minute prediction horizons in one hour ahead. The travel time is calculated as the ratio of the length 21 of each link and the harmonic average speed for all reporting vehicles . Hence , a model is trained 22 for each technique to predict travel time 5 min utes ahead, 10 min utes ahead, and all the way down 23 to 60 min utes ahead. The results confirm satisfying performance of both models in short -term 24 travel time prediction with slightly outperformance of Random Fo rest model . A feature importance 25 and sensitivity analysis also applie d for the Random Forest model, and traffic variable s are found 26 as the most effective variable s in predicting the travel time. 27 28 Keywords : Travel Time Prediction , Machine Learning, K -Nearest Neighbors, Random Forest , 29 Feature Importance 30 1 Corresponding author. Tel: +1 (312) 996 -9840. Email addresses : htaghi2 @uic.edu (H. Taghipour ), aparsa2@uic.edu (A. B. Parsa), kouros@uic.edu (A. K. Mohammadia n). 2 Introduction 1 Nowadays, having sufficient information about travel time plays a pivotal role for both road users 2 and traffic engineers. Hardly is it possible for road users to make decisions before and during their 3 trips, unless they have access to reliable information about travel time of the route they are passing 4 through . Having access to accurate travel time will also enable traffic engineers to increase the 5 efficiency and safety of traffic in road networks (Van Lint, 2008) . Travel time can be defined as 6 the total time required for a vehicle to pass a route from one point (origin) to another point 7 (destination) with respec t to all the delays which might be imposed on that vehicle (Zhu et al ., 8 2009) . Currently, the travel time which is reported on Variable Message Signs (VMS s) of some 9 highways is estimated by employ ing naïve methods that use limited sources of data (Huisken and 10 van Berkum, 2003) . This might cause inaccurate travel time p rediction, and impose delay on 11 traveler s, which consequently make s the travel time reports on the VMSs unreliable. Therefore, 12 the objective of this stud y is considering the effect of different factors such as weather condition, 13 accidents, road works, and special events, which are rarely considered in other studies, on travel 14 time of highways by combin ing these real-time sources of data and employing machi ne learning 15 methods . 16 Studies aimed at finding tr avel time could be divided into two main groups of estimating 17 and predicting travel time (Mori et al., 2015) . Travel time estimation studies focus on calculating 18 travel times of trips that have already finished, with respect to the dat a captured during those trips 19 (Celikoglu, 2013; Li et al., 2013; Soriguera and Robuste, 2010) . On the other hand, travel time 20 prediction studies, are concentrating on using the historical and real time data to forecast the travel 21 time for the future time intervals (Van Lint, 2004) . There are several methods which are used for 22 travel time prediction, and the difference between methodologies stems from their complexity and 23 accuracy , that make each of them suitable for specific conditions. 24 Basically, travel time prediction models could be categorized in to three main groups. The 25 first group are naïve models which generally consist of very simple methods (Huisken and van 26 Berkum, 2003) . These models have several assumptions in their nature which make them simple 27 but less accurate (Van Lint, 2004) . Thus, these models are generally used as the baseline for the 28 other methods or whenever just a simple prediction is required. One of the famous naive models 29 is instantaneous predictor, which assumes the traffic condi tion remains consistent with the passage 30 of time (Van Lint and Van Hinsbergen, 2012) . Based on this assumption, the most updated 31 estimation of travel time is assumed to be constant and equal to the travel time in the future. It is 32 obvious that expanding the prediction horiz on will reduce the accuracy of this type of model. 33 Another kind of naïve predictor uses the historical travel time data to predict the future travel time 34 regarding the similarity of traffic condition in different periods of time (Schmitt and Jula, 2007) . 35 This model works well when recurrent traffic conditions occur in a specific path (Van Lint and Van 36 Hinsbergen, 2012) . 37 Second group is traffic -theory -based models. The basis of this group of models is on the 38 relation between traffic variable s such as speed, density, and occupancy. Since the prediction refers 39 to travel time in future, the first step in these models is to simulate the traffic condition of a network 40 for a given time in the future and then travel time could be predicted based on traffic condition of 41 that network (Mori et al., 2015) . Regarding this issue, three kinds of simulation com prise d of 42 macroscopic (Papageorgiou et al., 2010) , microscopic (Edara et al., 2017) , and mesoscopic (Taylor, 43 2003) are applied in various studies. 44 The last group of models, which are data -based models, require a large amount of da ta and 45 use sta tistical methods to predict travel time. The more the data is available, the more accurate the 46 result will be. These models could be divided into two main groups of parametric and non - 47 3 parametric models (Mori et al., 2015) . Parametric models have a pre -defined function and just the 1 parameters of the model need to be estimated. There are several parametric models each of which 2 has their own structure such as Linear Regression (Du et al., 2012) , Bayesian Nets (Castillo et al., 3 2011) , and Time Series models (Billings and Yang, 2006; Karimpour et al., 2017; Yang, 2005) . 4 Non-parametric models are another part of data -based models in which the structure of the function 5 should be defined based on the data as well as number and typology of parameters. The most 6 important goal of these methods is to utilize a large database and l earn the relationships between 7 variable s, instead of using complex approaches, in order to predict travel time. One of the most 8 famous group of models includes Artificial Neural Network model s which have been widely used 9 in transportation studies (Dharia and Adeli, 2003; Fan and Gurmu, 2015; Parsa et al., 2020a, 2020b, 10 2019a, 2019b) . Specifically, K-Nearest Neighbors (Qiao et al., 2013; Yu et al., 2016; Zhao et al. , 11 2018) , Regression Trees (Nikovski et al., 2005) , Local Regression (Simroth and Zahle , 2010) , 12 Support Vector Regressi on (Wu et al., 2004; Yildirimoglu and Ozbay, 2012) , and Random Forest 13 (Leshem and Ritov, 2007) are non -parametric models which are performing well for travel time 14 prediction . 15 One of the most important and effe ctive factors on choosing a method for estimating or 16 predicting travel time is the available data. Although there are several kinds of methods each of 17 which has its own advantages and disadvantages, none of them could be u sed unless adequate and 18 related so urces of data are available. Based on the classification done by Wu, et al. (Wu et al., 19 2004) , there are several kinds of sensors using for obtaining traffic d ata and these sensors can be 20 group ed with respect to their ability to obtain travel time directly. Accordingly, t wo main groups 21 of sensors are link -based and point -based sensors. Link-based sensors measure the travel time 22 directly for a route or link by passive probe vehicles, active test vehicles, or license -plate matching. 23 In point-based sensors travel time is measured indirectly by devices such as loop detectors, laser 24 detectors, and video cameras. The data which is collected from link -based sensors are more 25 accurate, however point -based sensors are available more and could collect cost -effective real- 26 time data. 27 Based on the characteristics of all different methods, since the main data source for this 28 study is collected from loop detectors, the data -based methods are employed in order to predict the 29 travel time. In general, to the best of the authors knowledge, most of studies in this area ha ve 30 considered only traffic related variable s in order to develop a model for travel time predict ion. 31 However, other variable s such as weather conditio n could have significant impact on the travel 32 time (Qiao et al., 2012; Rakha et al., 2012) , which is infrequently investigated in travel time 33 prediction studies. N ookala showed that inclement weather condition could increase the traffic 34 congestion and travel time, since the capacity of highway decreased in inclement weather, while 35 the traffic dema nd does not change (Nookala, 2006) . There fore, the main goal of this study is to 36 predict travel time for links of Eisenhower highway in the city of Chicago, by utilizing a 37 combination of different sources of data including loop detectors, probe vehicles, weather 38 condition, network, accident occur rence, road works, and special events. To this end , average 39 speed of each link , and consequently travel time is predicted using KNN and Random Forest 40 models. 41 In what follows, first the datasets and the process of cleaning and combining them, which 42 are used in this study, are introduced. Also, the methods which are employed to predict the travel 43 time are described. Then, the results of the models are shown and explained. In the end, the 44 discussion of the results, conclusion and limitations of the study are d iscussed. 45 46 4 Data 1 Loop Detectors Data 2 In this study, differ ent sources of data are used in order to develop models to predict travel time. 3 The main part of dataset is the information captured by inductive loop detectors in the Chicago 4 highways network which is collected by the Gateway Traveler Information System and provided 5 by the Illinois Department of Transportation (I DOT). For this study, a 5 -mile segment from west 6 bound of the Eisenhower highway, consist of 14 loop detectors is selected. For each loop detector 7 in each lane of the highway , number of vehicles, occupancy and average speed are reported every 8 20 second. These raw data points contain lots of missing and erroneous records which might be 9 caused by detectors' malfunction, deterioration of pavem ent or any other reasons . Therefore, two 10 types of single and combined thresholds are applied to the dataset , as a part of data cleaning 11 procedure . 12 13 Single Threshold 14 There are several thresholds for each of the count , occupancy and speed values which are us ed in 15 different studies . In this study, based on the characteristics of the selected highway, value of 3000 16 vehicles per hour per lane is set as the threshold for the count , which is almost equal to 17 vehicles 17 per 20 second per lane. So, if the value of count in a lane does not lie within the domain of (0, 17), 18 it is labeled as erroneous data point. Moreover , the extreme amounts for occupancy ( i.e., more than 19 90% in 5 minutes) are assumed incorrect so that any data point with occupancy value outside the 20 domain (0, 90) is labeled as erroneous data point. In addition , we set the value of 80 mph for the 21 upper threshold of speed, and any point with more than 80 mph or less than 0 mph speed is 22 considered as an outlier. 23 24 Combined Threshold 25 As mentioned earlier, in this study three variable s of count , occupancy and speed are reported by 26 loop detectors. The first threshold in this group is described as “only one zero variable out of three”, 27 which means if only one of th ose three variable s is zero whereas the other two are non -zero, that 28 record is reasonably considered as an erroneous record . The second combined threshold is “o nly 29 one non -zero variable out of three ”. So that if two variable s out of count , occupancy and speed are 30 zero while the other one is non -zero, again this record of data is labeled as an error . Finally , the 31 last combined threshold is “a ll zero variable s”, in which a record is considered as an erroneous 32 record if all the variable s of count , occupancy , and speed are zero . This threshold filters out the 33 data points that could impact the average speed of intervals incorrectly . 34 35 Data Imputation 36 The missing and erroneous data points could negatively affect the results of prediction models 37 (Karimpour et al., 2019) . Therefore, these points should be either eliminated from the dataset or 38 imputed by new data points through one of th e three following ways. First, “temporal estimation ” 39 is used w hen a detector has a missing data point, or it reports a value which exceeds threshold for 40 an interval . In this case, an appropriate way for imputi ng the data point is taking the average of 41 values for previous and next time intervals , especially when the time intervals are short ( i.e., in 42 this study it is 20 second). In another approach , which is “spatial estimation ”, the missing or 43 erroneous data points are imputed with the average of the values for previous and next loop 44 detectors at the same time . This metho d is proper whenever loop detectors are close to each other , 45 or using the temporal estimation is not applicable . Finally, “historical estimation ” could be used 46 whenever traffic condition is recurrent in a location in which missing or erroneous data is repo rted. 47 5 So, in this case the erroneous data points are imputed using the reported data points from same 1 location for same time of day and same day of week in the past. Figure 1 shows a sample of weekly 2 pattern of the average flow after applying data imputati on process for the studied segment s. This 3 figure display s 7 daily traffic patterns, started from Monday, September 11, 2017 to Sunday, 4 September 17, 201 7. It is observed that weekdays have almost similar traffic patterns although the 5 traffic pattern is dif ferent for Friday, Saturday, and Sunday. 6 7 8 Figure 1 Weekly pattern of average flow from cleaned loop detectors dataset 9 10 In this study, the speed values in the loop detectors dataset are time mean speed s. The time 11 mean speed is calculated by averaging the point speeds of all vehicle s passing a loop detector at 12 that detector ’s locat ion. This speed could not reflect the travel time of a path segment accurately , 13 since the speed of vehicles could vary across that segment. Therefore, in this study in order to 14 consider the speed values another source of data which is collected by probe vehicles is utilized . 15 16 Probe Vehicles Data 17 This dataset is provided by the National Performance Management Research Data Set (NPMRDS) 18 and includes the harm onic average speed , free flow speed, historical speed, travel time , and annual 19 average daily traffic (AADT) for each link of highways. INRIX is creating the NPMRDS dataset 20 of average speeds and travel times for specific road segments across the NPMRDS road network 21 for every 5 minute s. INRIX uses its large data source includes millions of connected vehicles and 22 trucks that are supplying location and movement data anonymously. They also implemented a 23 path-processing algorithm to meet the NPMRDS requirements u sing its existing source data. 24 For the present study, value of travel time from this dataset is chosen to be the target 25 variable . This variable is defined as the ratio between the segment length and the harmonic average 26 speed of vehicles passing the segme nt. Since the travel time of each link is dependent to length of 27 the link, the harmonic average speed is selected as the target, so that by pred icting the average 28 speed, value of travel time could be calculated using the length of each link. The missing va lues 29 of this dataset are imputed by temporal estimation, spatial estimation, and historical estimation , as 30 it is explained in the previous secti on. Figure 2 shows histogram of the average speed from this 31 dataset. 32 33 6 1 Figure 2 Averag e speed histogram from probe vehicles dataset 2 3 Weather Condition Data 4 The hourly weather condition data which is provided by the National Weather Service (NWS) is 5 another source of data used in the present study. This data is available for airport stations and based 6 on the location of selected segment, data f rom the Midway International Airport is utilized . In the 7 dataset, weather condition is reported by 94 different states . In this study, a ll the states are grouped 8 into four categories from fair to severe weather condition s. In the first category, weather conditions 9 such as “Fair” and “ Partly Clou dy” are grouped . The second category is comprised of weather 10 conditions like “Fair with Light Haze” and “ Light Haze Dust and Windy”. Unfavorable weather 11 condition s such as “Light Drizzle” and “ Light Rain Fog ” constitute the third category . Finally, the 12 fourth and the most severe category includes weather conditions like “ Snow Fog ” and 13 “Thunderstorm Heavy Rain Fog ”. In addition to the weather condition, surface temp erature is also 14 available in the dataset which is used in this study. Simila r to other data sources there are plenty 15 of missing values in this dataset which are imputed using the information from previous and next 16 time intervals . 17 18 Accident Data 19 This datase t is also collected by the Gateway Traveler Information System . In t he accident data 20 details of accidents occurred in highways are reported . Time of accident, geographical location, 21 and severity of accident are among the most important variable s in this da taset. 22 Based on the accident data analysis and observing its effect on highway traffic at the studied 23 area, when an accident occurs on a highway link, that link, the previous link, and the next link on 24 the highway are affected on average for one hour aft er the accident occurr ence. This effect is 25 considered as binary variable s to show the affected links and time intervals in the model. Two 26 binary variables for previous link and next link are defined, s ince the effect of accident is different 27 in upstream an d downstream. Also, another variable in the model demonstrates the accident 28 severity in three levels of minor, medium and major. Whenever the accident severity is not 29 determined at accident detection time, the medium level, which is the most co mmon one , is 30 selected. 31 32 Road Work Data 33 This dataset which is collected by the Gateway Traveler Information System , is comprised of 34 details of construction or maintenance operations in the highways. In this dataset variable s such as 35 start time, end time, ge ographical l ocation, and severity are reported. 36 In order to consider the effect of this variable in the model a binary variable is defined. 37 7 When a roadwork project is executing on a link of highway, that link, the previous link, and the 1 next link are affected from st art time to end time of the project . Also, if one of the lanes of highway 2 is blocked, variable of “number of lanes” considers its effect in the model. 3 4 Special Event Data 5 The special event dataset collected by the Gateway Traveler Information System provi des detailed 6 information about stadium events, parades, and road races. In this dataset several variable s such 7 as start time, end time, location, and type of events are reported. 8 When a special ev ent is occurring close to highway links, those links are co nsidered to be 9 affected by that event for one hour before or one hour after that event. Considering the next or 10 previous one -hour depends on the direction of extra traffic and the relative location s of the event 11 and each link. For example, if a highway link is located in the path toward an event location, the 12 extra traffic passes th at link before the event in order to go to that location, so in this case we 13 consider that link to be affected by the event one hour before start of the event . For the links whic h 14 are located in the path from the event location toward other parts of the city, one hour after end of 15 the event would be the affected time. Since these variable s can considerably impact travel time of 16 a route, we combine and use them in the model. 17 18 Netwo rk Data 19 This dataset includes several variable s from the studied highway segment such as number of lanes, 20 number of entrances, number of exits, and length for each of the highway links. 21 22 After applying data imputation process on each of the mentioned data sets, all of them are 23 combined based on their location and timestamps, so that a link-based dataset which is aggregated 24 in 5 minutes, from April 2017 to December 2017 is created for 15 links of west bound of the 25 Eisenhower highway. Table 1 demonstrates the varia bles of the final combined dataset which is 26 used for developing travel time prediction models. 27 28 Table 1 Description of explanatory variable s 29 Variable Description Time i i= 1, 2, …, 8 1: if time ID equals i*, and 0: otherwise Friday 1: if the day of week is Friday , and 0: otherwise Saturday 1: if the day of week is Saturday , and 0: otherwise Sunday 1: if the day of week is Sunday , and 0: otherwise WeatherID Weather condition : 1: Fair or a few clouds 2: Cloud y with haze or light fog 3: light rain or drizzling 4: Heavy rain, snow, thunderstorm or fog Temp_F Temperature (Fahrenheit) 8 Occupancy Occupancy_p Occupancy_n Aggregated o ccupancy at location of loop detector which is closest to midpoint o f the link at the 5-minute time interval Aggregated occupancy at location of previous loop detector on the road at the 5- minute time interval Aggregated occupancy at location of next loop detector on the road at the 5-minute time interval Count Count_ p Count_n Aggregated count of vehicles on loop detector which is closest to midpoint of the link at the 5-minute time interval Aggregated count of vehicles on previous loop detector on the road at the 5-minute time interval Aggregated count of vehicl es on next loop detector on the road at the 5-minute time interval AADT Annual average daily traffic of the link EntRamp Number of entrance ramps on the link ExtRamp Number of exit ramps on the link Miles Length of the link in miles Lanes Number of lanes on the link IsAcc_p 1: if an accident occurred on the link or previous link, at the 5 -minute time interval, and next one -hour time intervals after accident occurrence, and 0: otherwise IsAcc _n 1: if an accident occurred on the link or next link at the 5-minute time interval , and next one-hour time intervals after accident occurrence , and 0: otherwise AccSeverityID Accident severity: 1: Minor severity 2: Medium severity 3: Major severity IsRW 1: if a roadwork project is execu ting on the link, previous link, or next link at the 5-minute time interval , and 0: otherwise IsSE 1: if a special event is occurring close to the link at the 5-minute time interval , and either next one-hour, or previous one -hour time inte rvals**, and 0: otherwise Speed (target) Average speed of vehicles on a link at the 5-minute time interval * Each 3 -hour time interval of a day is considered as a binary variable . For instance, Time1 means from midnight to 3 am, and Time8 represents interval of 9 pm to 12 am . ** Considering the next or previous one -hour depends on the direction of extra traffic and the relative location s of event and link. For example, if a link is located in the path toward an event , the extra traffic pass es this link before the even t, so in thi s case we consider previous one -hour as the effective time . 9 Methods 1 Machine learning methods are compatible with the type of data that is available for this study, in 2 order to develop the travel time prediction model. Among different machine l earning methods K- 3 Nearest Neighbors and Random Forest are selected based on their specific properties , which are 4 described as follows . 5 6 K-Nearest Neighbors 7 K-Nearest Neighbors (KNN) needs only a large volume of data points, and without developing a 8 mathema tical model could predict the target variable. Also, there is no need to define parameters 9 of the model in advance, and the authenticity of the data is maintained since no smoothing 10 procedure is done to the data. Therefore, this method is an appropriate ch oice for the nonparametric 11 problem of travel time prediction (Yu et al., 2016) . 12 This method is among the most famous supervised machine learning techniques , which are 13 widely used for regression and classification. As it is explained by Han et al. (Han et al., 2011) , 14 the KNN method basically classifies a dataset by comparison to the similar records of a trained 15 dataset. In this study , the KNN regression algorithm is used, since the target is speed which is a 16 continuous value rather than a predefined class. In general, training dataset has n attributes , and 17 each of its records could be presented in a n -dimensional variable space by the value of the n 18 attributes. After tha t, to predict the target value for new record s from test dataset, each new record 19 with its attribute values finds its location in the variable space , and the algorithm looks for the k 20 nearest neighbors that are closest to the new record among the trained data points. Therefore , in 21 this study the speed value for a new record would be predicted by taking average of the speed 22 values of k nearest neighbors to that record . In other words, the method searches for nearest 23 neighbors among all historical data points and uses them for the prediction. 24 In this study, the Euclidean metric is used to calculate the distance between two points in 25 the n -dimensional variable space. Betw een two points of 𝑋1=(𝑥11,𝑥12,...,𝑥1𝑛) and 𝑋2= 26 (𝑥21,𝑥22,...,𝑥2𝑛), the Euclidean distance is defined as Equation 1 : 27 28 Euclidean distance (𝑋1,𝑋2)=√∑ (𝑥1𝑖−𝑥2𝑖)2 𝑛 𝑖=1 (1) 29 30 Before calculating the distance between p oints, values of attributes are normalized in order 31 to prevent attributes with larger values like occupancy outweighing attributes with smaller values 32 like binary attributes (Han et al., 2011) . 33 34 Random Forest 35 Random Forest is an ensemble supervised machine learning method which could be used as 36 classifier or regressor for categorical or numerical datasets (Han et al., 2011) . In this method a 37 combination of several random Decision Trees (DT) are utilized , so that each of the DTs votes to 38 predict value of the target variable . In order to combine DTs, Bootstrap Aggregation or bagging 39 that is a model averaging approach for combining machine learning methods to increase accuracy 40 is used. To develop a Random Forest model multiple random sample s from training dataset are 41 selected with replacement in several iterations , and for each of them a DT is trained. Then, for a 42 new record from tes t dataset, each of the trained DTs returns value of the target variable . The final 43 result is calculated by taking average of all the predi cted values for the target from DTs. Random 44 Forest is robust to noisy data and overfitting, also it is expected to have higher accuracy than 45 individual DT, since it decreases variance of the DTs (Han et al., 2011) . Random Forest typically 46 works accurately and fast when a large dataset is available. Also, it could manage large number of 47 10 variable s as the model inputs. Therefore, these ch aracteristics make the Random Forest model a 1 proper choice for predicting the travel time in this study (Fan et al., 2018) . 2 Results 3 After ap plying data cleaning process on different dat a sources and combining them to create the 4 final dataset , 80% of the data points are randomly selected for model training and the remaining 5 20% are used as the test data to evaluate the models. In addition, numb er of neighbors for the KNN 6 model is assigned experimentally as 4, and for Random Forest model, 20 DTs in forest with max 7 depth of 30 are set. Finally, KNN and Random Forest models are trained to predict and compare 8 short -term average speed as a representa tive of travel time, as mentioned in previous sections, for 9 5-minute prediction horizons in one hour ahead. That is, a model is trained for each technique to 10 predict travel time at the moment, 5 min utes ahead, 10 min utes ahead, and all the way down to 60 11 minutes ahead. 12 Figure 3 displays and c ompare the prediction accuracy (i.e., R -Squared Score) of KNN 13 and Random Forest models. Random Forest model outperforms KNN model in all 13 prediction 14 time intervals, however, the difference between their prediction acc uracy is more negligible for 15 very sho rt-term prediction horizons. Both models perform extremely good at 0 min ute ahead (i.e., 16 at the prediction moment which would be travel time estimation ), however, Random Forest with 17 prediction accuracy of 95.3% slightly outperforms KNN with prediction accu racy of 9 2.9%. Then, 18 prediction accuracy is decreasing by increasing prediction horizon in a way that prediction 19 accuracy at 5 min utes and 10 min utes ahead for Random Forest model are 93.9%, and 91.6%, 20 respectively, and those of KNN model are 91.1%, and 88.1%, respectively. Another point based 21 on Figure 3 is that prediction accuracy of both models is decreasing in a faster rate in the first 30 22 minutes ahead horizon than the horizon of 30 min utes to 60 min utes ahead. Desp ite the plateauing 23 effect for prediction horizons above 30 min utes of both models, Random Forest model 24 performance is more robust as it achieves prediction accuracy of 84% at 60 min utes ahead while 25 that of KNN model is only 76.8%. 26 27 28 Figure 3 Comparison of KNN and Random Forest models’ accuracy in different prediction horizons 29 30 11 Regarding Figure 3 , it can be concluded that both models, especially Random Forest, are 1 well-suited to predict travel time in a short -term horizon (i.e., 15 minutes ahead). Therefore, to 2 better observe performance of these models in short -term prediction, true speed values versus 3 predicted speed values of KNN, and Random Forest models are plotted in the Figure 4 . This figure 4 confirms that the shorter predicti on horizon, the more prediction accuracy for both models. It is 5 worth noting that, regarding training time, Random Forest model outperforms KNN mode l as well. 6 Accordingly, feature importance analysis is applied for the Random Forest model, and the 7 result i s shown in Table 2 . As it is expected the traffic variables are the most important variables 8 that affect the travel ti me. After traffic variables, temperature is found effective in the model which 9 shows the importance of weather condition variable in traff ic related predictions. Unlike our 10 expectation, accident, special event, and road work incidents have relatively lower impact in the 11 model. However, the reason for low importance of these variables might stem from small number 12 of these incidents in the dat a. It is worth noting that since these incidents are correlated with traffic 13 variables which are important inputs of t he model, their effects are considered indirectly through 14 the traffic variables in the model. 15 16 Table 2 Feature imp ortance results 17 Variable Rank Occupancy 1 Count_p 2 Occupancy_p 3 Count 4 Occupancy_n 5 Count_n 6 AADT 7 Temp_F 8 Miles 9 Time3 (Morning Peak -Hours) 10 Time7 (Evening Peak -Hours) 10 Sunday 11 Saturday 12 Friday 13 ExtRamp 14 Timei (i = 1, 2 , 4, 5, 6) 15 EntRamp 16 AccSeverityID 17 WeatherID 18 Lanes 19 Evets (IsAcc, IsRW, IsSE) 20 18 12 1 Figure 4 True speed values versus predicted speed values – (a) KNN, (b) Random Forest 2 3 The feature importance analysis could only relatively rank the importance of variables in 4 the model, therefore in order to assess the potential impact of the most important variables on the 5 (a) (b) At the moment 5 minutes ahead 10 minutes ahead 15 minutes ahead 13 target variable a sensitivity analysis is also conducted. To this end, the top three important variables 1 in the list which are occupancy ( Occupancy, Occupancy_p, and Occupancy_n ), count (Count, 2 Count_p, and Count_n) , and AADT are selected . Then , the final Random Forest model was run 3 several times with change in each of these variables by  10%,  20%,  30%,  40%, and  50% 4 while other variables remain the same. Figure 5 demonstrates the sensitivity of average speed for 5 all the links and all the time inter vals to the important variables. Based on the figure, it indicates 6 that increasing the occupancy reduces the average speed , and it shows a linear relationship as it is 7 expected. For the count variable, a nonlinear relationship is observed. As it is mention ed, the 8 average speed value in here is for all the links and times in different conditions . Therefore, on 9 average the speed is increasing by increasing the count from -25% to +50% , also by decreasing 10 the count from -25% to -50%. The effect of AADT on the average speed is also nonlinear, that is 11 by increasing the AADT the average speed increases, but there is no significant change in average 12 speed by increasing the AADT from +10% to +50%, and from -50% to -10%. 13 14 15 Figure 5 Sensitivity analysis of important variables in travel time prediction model 16 Summery and Conclusions 17 In this study mul tiple data sources including loop detectors, probe vehicles, weather condition, 18 network, accidents, road works, and special events are combined, and two machine learning 19 methods comprised of KNN and Random Forest are employed for short -term prediction of t ravel 20 time for highways. Accordingly , a model is trained for each technique to predict travel time 5 21 minutes ahead, 10 minutes ahead, and all the way down to 60 minutes ahead. 22 Comparison of Random Forest and KNN models shows good performan ce of both models 23 in short -term travel time prediction with slightly outperformance of Random Forest model for very 24 short -term prediction horizons. Therefore, applying Random Forest model for 15 min utes or less 25 prediction horizons is more reasonable althou gh the prediction accuracy of longer prediction 26 horizons is still acceptable. 27 Traffic related variable s, especially occupancy , are found so effective in short -term travel 28 time prediction using loop detector data. It suggests that among traffic variable s collected by loop 29 -20-15-10-505101520 -50 -40 -30 -20 -10 0 10 20 30 40 50Percent Change in Average Speed Percent Change in Important VariablesOccupancy Count AADT 14 detectors, occupancy can capture traffic condition better that the others. However , fusion of several 1 traffic data from different sources could increase prediction accuracy of the models. 2 Weather condition is an important variable in the studies related to traffic condition. In this 3 study, we utilized weather condition in our models and found it effective in predicting travel time. 4 It is suggested that to better capture impact of weather condition in the model, data collected f rom 5 more accurate sensors ar e required for the similar studies. 6 Finally, accident, special events, and road works are important variable s which can have a 7 considerable impact on travel time prediction. Although in this study these variable s have 8 relatively less impact on travel time prediction, for the future studies we suggest that rich amount 9 of incident data along with dynamic models could accurately capture the impact of these variable s 10 on short -term travel time prediction. 11 Acknowledgement 12 Authors would like to show their gratit ude to the Illinois Department of Transportation (IDOT) for 13 funding this project and providing the data sources. 14 15 15