Improving Non-motorist Safety at Gated Railroad Crossings Potential Application of Intelligent Transportation Systems

Improving Non-motorist Safety at Gated Railroad Crossings Potential Application of Intelligent Transportation Systems 1 Aemal Khattak, 2 Zheng Luo 1 Department of Civil Engineering, University of Nebraska-Lincoln, 330E Whittier Research Centre, 2200 Vine Street, Lincoln, NE 68583 USA 2 Scheffer Andrew Ltd.12204 145 Street NW Edmonton, AB T5L 4V7 Canada 1 khattak@unl.edu; 2 alexro1981@hotmail.com Abstract- This research evaluated a short-term educational campaign in improving non-motorists (pedestrians and bicyclists) safety at a gated highway-rail grade crossing. Such campaigns may be useful to undertake prior to activities planned in proximity of highway-rail crossings, e.g., parades, marches, gatherings, etc. Additionally, this research explored the potential for application of Intelligent Transportation Systems (ITS) at the gated highway-rail grade crossing by investigating the relationship between train warning times (i.e., the elapsed times between onset of flashing lights at the crossing and train arrivals at the crossing) and nonmotorists gate violations. Evaluation of the educational campaign effectiveness involved observing non-motorists passing around fully-lowered crossing gates (with trains approaching the crossing) for seven days before and seven days after the campaign at the crossing. A before-after comparison of non-motorist gate violations showed a statistically significant reduction in gate violations after completion of the educational campaign. However, the analysis failed to show a statistically significant relationship between gate violations and train warning times. A discussion of the study limitations and future research is provided in the paper. Keywords- Safety; Pedestrians; Bicyclists; ITS; Rail Crossings I. INTRODUCTION The issue of non-motorist safety at highway-rail grade crossings (HRGCs) in the United States (US) is important; the 2010 US Federal Railroad Administration (FRA) statistics showed 143 pedestrian crashes reported at HRGCs nationwide with 79 fatalities and 48 non-fatal injuries (FRA 2012). The ten-year pedestrian crashes reported at HRGCs show an increasing trend (Fig. 1). Statistics for crashes involving bicyclists at HRGCs are not readily obtainable because form FRA F6180.57 (used for reporting crashes at HRGCs in the US) does not include a bicyclist category for type of highway user involved in a HRGC crash. Bicyclists are usually coded as highway user type other in the FRA database along with a variety of disparate users. Nonetheless, bicycling is increasing across the US and bicyclists safety at HRGCs is as important as the safety of other users of HRGCs. Fig. 1 Ten-year trend in pedestrian crashes at HRGCs (Source: FRA) The objectives of this research were to assess the effectiveness of a short-term educational campaign in reducing nonmotorists gate violations at a gated HRGC and to explore the potential use of intelligent transportation systems (ITS) at HRGCs for improvement of non-motorist safety by investigating the relationship between train arrival times (i.e., the elapsed times between onset of flashing lights at the crossing and train arrivals at the crossing) and gate violations by non-motorists. In this research pedestrians and bicyclists at HRGCs were jointly referred to as non-motorists. Short-term educational campaigns at HRGCs may be useful to undertake prior to planned events e.g., parades, marches, gatherings, celebrations, etc. While not necessarily involving bicyclists and pedestrians, tragedies such as the Midland, Texas HRGC crash on November 15, 2012-8 -

may be preventable provided educational campaigns are effective in improving HRGC safety. The Midland, Texas crash involved a freight train that struck a flatbed trailer being used as a parade float carrying 26 passengers (12 of them US Armed Forces veterans), killing four veterans and injuring sixteen others. Therefore, there is a need to look into the effectiveness of user education in improving HRGC safety. The methodology consisted of observing gate violations by non-motorists at a selected crossing for seven days before and seven days after a short educational campaign that was conducted at the crossing. The educational campaign lasted for two days and involved on-site showing of Operation Lifesaver s safety videos and distribution of Operation Lifesaver s educational materials including informational brochures, DVDs with highway-rail crossing safety information, children s coloring and activity books, and HRGC safety themed promotional items (e.g., key chains, buttons, train whistles, water bottles, etc.). For information on Operation Lifesaver, the reader refers to www.oli.org. The effectiveness of the educational campaign was judged by a statistical comparison of non-motorist gate violations before and after the educational activity. No other safetyenhancing measures (e.g., enforcement or traffic diversion) were undertaken during the course of this research. Therefore, the changes observed in non-motorists gate violations, after accounting for other factors affecting gate violations, were attributed to the undertaken educational campaign. Potential for ITS technologies was assessed by investigating the relationship between non-motorists gate violations and train warning times. It was hypothesized that excessively long warning times were accompanied by more frequent gate violations by non-motorists as they got tired of waiting for trains to arrive. ITS technologies can improve the situation by reducing excessively long warning times. While a minimum train warning time of 20 seconds is mandated in the US, the actual warning times experienced by users vary depending on the type of warning system installed, train speed, and train acceleration/deceleration. If excessively long warning times are associated with more frequent HRGC gate violations by nonmotorists, then ITS detection and communication technologies may potentially improve safety at HRGCs by optimizing train warning times. For this research a gate violation was defined as passing around fully deployed HRGC gates (i.e., in a horizontal position) when trains were approaching toward the crossing. A salient aspect of this research was keeping counts of the number of available opportunities for violations during train crossing events as more gate violations were expected with greater opportunities for gate violations. An opportunity for a gate violation was deemed available when a non-motorist arrived at the HRGC with fully deployed gates while the approaching train was not yet at the crossing. The organization of this paper is as follows. After this introduction a review of pertinent literature on the effectiveness of educational activities in improving safety and train warning technologies used at HRGCs is presented. Research data collection is described after the literature review. The next section describes the analysis of collected data. Conclusions and a discussion on applicability of ITS in improving non-motorist safety at HRGCs are offered after the analysis section. A reference section concludes this paper. A. Education II. RELEVANT LITERATURE Literature on educational activities at HRGCs was reviewed as part of this research. Richards and Heathington (1988) conducted surveys in Tennessee to evaluate motorist comprehension of HRGC traffic control devices and traffic regulations. They reported that most drivers indicated a need for increased education besides more grade separations and installation of gates and flashing lights. In another study Bowman, Stinson and Colson (1998) reported a plan to reduce HRGC crashes in Alabama. They recommended Operation Lifesaver education via mass media and distribution of brochures at all state driver license locations. Lobb, Harre, and Suddendorf (2001) evaluated a program of education and access prevention designed to reduce unsafe crossing of rail tracks in Auckland, New Zealand. Immediately after the program implementation the proportion of those crossing the rail tracks by walking across rather than using a nearby bridge decreased substantially. Three months later the decrease was even greater. Since both educational and access prevention interventions were introduced simultaneously, the effects of the two interventions could not be separated nor could other unmeasured factors be ruled out. Anonymous surveys administered immediately before and three months after the interventions indicated that while awareness of the illegality of walking across the tracks had increased slightly, perception of risk had not changed. This suggested that the educational interventions had less effect than the access prevention measure. In another study by Lobb, Harre, and Terry (2003) a comprehensive program of communication, public safety awareness, education and penalties amongst school children for improving rail crossing safety was introduced. For awareness a large billboard was placed near a crossing with a picture of a thinking schoolboy and safety warning words. The educational part included talks to students and follow-up activities related to crossing safety. The penalty was possible detention given by teachers if they observed students crossing in unsafe manners. The study reported a significant decrease in unsafe crossings after implementation of the program. Comparisons between different program parts showed that unsafe crossings were reduced between communications and education and even more so between education and implementation of penalties. The study - 9 -

reported safety improvement after implementation of the program and showed that penalties for unsafe behaviours were more effective than education and communication. A study by Mok and Savage (2005) showed that Operation Lifesaver public education campaign led to about one seventh reduction in the number of collisions at HRGCs experienced since 1975. In another study Savage (2006) investigated the relationship between yearly Operation Lifesaver activities across different states and the number of collisions and fatalities at highway-rail crossings. The study reported that increasing the amount of educational activities reduced the number of collisions but the effect of education on the number of fatalities could not be concluded with statistical certainty. The Public Education and Enforcement Research Study (PEERS) is a collaborative effort between the FRA, the Illinois Commerce Commission, and several communities in Illinois. As part of PEERS, Sposato, Bien-Aime, and Chaudhary (2006) reported on crossing safety in the Arlington Heights (IL) community where education and enforcement activities targeted at reducing violations at grade crossings were undertaken. Three gated HRGCs in this community saw an overall reduction of 30.7% in violations between the pre-test to the post-test period. The largest reduction, 71.4% was reported in the most risky type of violation traversing the crossing after the gates were fully deployed in a horizontal position. Overall highway user behaviour changed for the safer and pedestrians, especially commuters, who were most affected by the PEERS program. Another study by Horton (2011) pertaining to the PEERS program implemented in the Macomb (IL) community showed that overall grade crossing violations were not reduced from the pre-test to the post-test period. Grade crossing violations continued at the same rate, or increased, throughout the tenure of PEERS. The reasons for diverse success levels of PEERS program in Arlington Heights and Macomb were attributed to differences in the population characteristics (Macomb had a higher turnover of student population), differences in highway users at HRGCs (Macomb had majority violations committed by motorists), and differences in wait times at HRGCs (Macomb had higher wait times). Another reason cited was the difference in the implementation of the PEERS program; Macomb s implementation was oriented toward passive activities to reach a wider portion of the community compared to Arlington Heights aggressive activitiesfocusing on the crossings. The study recommended development of a report on best practices and guidance on proper design of a successful crossing safety education and enforcement program. Transport Canada developed a pedestrian grade crossing safety guide (Transport Canada, 2007) that emphasized education via the Operation Lifesaver program besides suggesting other strategies for improving pedestrian safety at grade crossings. FRA provided guidance on pedestrian safety crossings at or near passenger stations (FRA, 2012). This document emphasized public outreach and crossing safety education programs (among other measures) based on the use of audio and video messages on station platforms coordinated with appropriate placement of posters. In summary, several published studies on effectiveness of educational activities in improving HRGC safety were reviewed. Most of the reviewed studies evaluated effects of educational activities along with other activities (e.g., enforcement); therefore the effects of those educational activities cannot be separated from the effects of the other activities. The next section presents an account of technologies used for train detection at HRGCs. B. Train Detection at HRGCs Detection of trains for the purpose of warning users of HRGCs can be broadly divided into three categories. Category 1 consists of equipment that is physically connected to rail track circuitry and provides a continuous signal until the presence of a train on the track changes this signal; this change then activates warning devices at the HRGC through a controller. Train detection equipment is placed at such a distance from the HRGC that the fastest train anticipated covers the distance in 20 seconds, thus ensuring the provision of the minimum warning time. Slower trains then produce warning times that are variable but exceed the minimum required warning time. An improvement to such a warning system involves the use of multiple detectors to estimate train speed and therefore provide less variable warning times. However, absence of information on train acceleration or deceleration can result in variable warning times for HRGC users. Interested readers refer to Tustin, Richards, McGee, and Patterson (1986) and to American Railway Engineering and Maintenance-of-Way Association (AREMA, 2000) for more details. Category 2 train detection technologies usually consist of video and radar detection that collect more information on trains. These technologies enable estimation of not only train speed but also acceleration/deceleration and therefore yield more accurate prediction of a train arrival at an HRGC. Limitations of these technologies include difficulty in obtaining measurements during rain, snow, and variable sunlight conditions. Additional details may be found in Estes and Rilett (2000) and in Cho and Rilett (2003). Category 3 detection technologies usually involve the use of the Global Positioning System (GPS) and are usually applied in train traffic management centres. Its use for train warning at HRGCs requires an integrated and consistent communication system, the presence of a GPS receiver on every train, and wayside interface units. Such detection technologies are not in use for warning users at HRGCs in the US. The next section provides information on the data collected for this research. III. DATA COLLECTION The study site for data collection was the M Street HRGC in Fremont, Nebraska (USDOT crossing 074662E, Fig. 2). This - 10 -

crossing is located in a residential area and has two sets of railroad tracks, two paved highway lanes, and is equipped with dual-quadrant gates. The gates have flashing lights, crossbuck signs, and an audible bell. According to the USDOT crossing inventory information, the estimated average vehicular daily traffic (1996) is 1,315 with 4% trucks and the average train traffic is 11 per day (although many more trains per day were observed during data collection). The maximum timetable speed for trains is 25 mph at this crossing. Fig. 2 The M Street crossing in Fremont, Nebraska For data collection, this HRGC was monitored for gate violations by non-motorists using a day- and night-vision camera and a digital video recorder (DVR). Two days (September 29-30, 2011) were dedicated to the educational campaign to ensure exposing non-motorists to the safety educational materials. Fig. 3 shows distribution of educational materials and interactions amongst research team members and the HRGC users while Fig. 4 presents a sampling of the Operation Lifesaver s educational materials distributed at the HRGC. Fig. 3 Distribution of educational materials at the HRGC Fig. 4 Sample Operation Lifesaver educational materials distributed at the HRGC - 11 -

Continuous video footage was captured one week before and one week after the educational campaign. Recorded video was subsequently reviewed in office for non-motorists gate violations during train crossing events. A train crossing event was defined by the elapsed time between the onset and cessation of flashing lights at the HRGC. Train crossing events without the presence of non-motorists at the HRGC were excluded from consideration. The total number of observations in the dataset was 97 of which 49 were collected during the before-education period (September 24-28, 2011) while 48 were collected during the after-education period (October 1-7, 2011). The dataset included counts of gate violations in each train crossing event and opportunities for gate violations available to non-motorists. Table 1 shows the complete list of collected variables including their respective coding/units. The next section provides information on data analysis. TABLE 1 VARIABLES IN THE COLLECTED DATASET Variable Description Coding/units N_Viol N_Opp NM_Traffic T_Arrival G_Down Count of non-motorists passing around fully deployed HRGC gates during a train crossing event Count of opportunities for non-motorists to engage in gate violations during a train crossing event Non-motorist traffic, i.e., number of non-motorists arriving at the HRGC during a train crossing event Elapsed time between onset of flashing lights and train arrival at the crossing Elapsed time between onset and cessation of flashing lights, i.e., total event time 0, 1, 2, 0, 1, 2, 1, 2, Seconds Seconds N_Trains Number of passing trains during a crossing event 1, 2, 3, T_Stop Indicator variable for train stoppage on the HRGC 1 if stopped, 0 otherwise Day Day of week of the train crossing event 1 if Mon, 2 if Tue,..., 7 if Sun Daytime Light condition at the time of train crossing event 0 if nighttime, 1 if daytime, 2 if dawn or dusk Period Indicator variable for time period 0 if before educational activity, 1 if after educational activity IV. DATA ANALYSIS Table 2 presents a simple comparison of the means of the non-motorists gate violations across the before and after educational campaign periods. It also presents comparative information on the available opportunities for engaging in gate violations in the two time periods. The before-after comparison of mean gate violations per train crossing event shows a reduction of 38.75% while an increase of 31.78% in opportunities for gate violations was recorded in the period after educational campaign. So, gate violations reduced despite an increase in opportunities to engage in such violations in the after educational campaign period. A more formal comparison using the Poisson regression model that accounted for impacts of other variables on counts of non-motorists gate violations was undertaken. An account of the Poisson model is given next while readers familiar with the details of this model may directly go the subsection B. TABLE 2 COMPARISONS OF GATE VIOLATIONS AND OPPORTUNITIES FOR GATE VIOLATIONS Variable Brief description Period N_Viol N_Opp Count of non-motorists passing around fully deployed HRGC gates during a train crossing event Count of opportunities for non-motorists to engage in gate violations during a train crossing event Mean violations per train crossing event Standard Deviation Before 0.51.68 After 0.31.62 Before 1.12.92 After 1.48.98 Percent change -38.75 31.78 A. Poisson Modelling of Gate Violations Counts of gate violations by non-motorists at HRGCs during train crossing events were modelled using the Poisson regression model (i.e., the dependent variable was N_Viol). Differences between the before and after educational activity periods were judged by inclusion of an indicator variable named Period (see Table 1 for coding of this variable) in the model specification. The Poisson model is appropriate to use when count data consist of nonnegative integer values and the mean and variance of the count variable are not significantly different from each other. The mean of N_Viol in the dataset was 0.41 violations per train crossing event with a variance of 0.43 violations per train crossing event squared. These two values were fairly close and the Poisson model was adopted for this analysis. According to Washington, Karlaftis, and Mannering (2011), the probability of a crossing event i having y i gate violations (y i 0), is given by: (1) - 12 -

Where is the probability of crossing event i having y i gate violations, e is the base of natural logarithm, and is the Poisson parameter for crossing event i, which is equal to crossing event i's expected number of gate violations, E[y i ]. Poisson models are estimated by specifying the Poisson parameter as a function of independent variables. The most common relationship between independent variables and the Poisson parameter is the log-linear model: Where is a vector of independent variables for crossing event i and is a vector of estimable parameters. This model is estimable by standard maximum likelihood methods with the logarithm of the likelihood function given as: [ ] (3) Marginal effects (evaluated at mean values) determine the effects of independent variables on a dependent variable. They provide an estimate of the impact of a unit change in a variable on the expected frequency of a count variable. The likelihood ratio test is used to assess competing models, usually a full or complete model over another competing model that is restricted by having a reduced number of model parameters. The likelihood ratio test statistic is: [ ] (4) Where is the log-likelihood at convergence of the restricted model, considered to have all parameters in β equal to 0 or just to include a constant term and is the log-likelihood at convergence of the unrestricted model. The statistic is chi-square distributed with the degrees of freedom equal to the difference in the number of parameters in the restricted and unrestricted model. A measure of overall model fit is the ρ 2 statistics given as: Where is the log-likelihood at convergence with parameter vector β and is the initial log-likelihood with all parameters set to zero. The value of varies between 0 and 1 and values closer to 1 indicate a better fitting model compared to values closer to 0. Results of the estimated Poisson model for frequency of gate violations are given next. B. Estimated Model Results Table 3 presents the estimated Poisson model for counts of gate violations per train crossing event. The model equation is: A positive coefficient in the above equation shows that counts of gate violations increased with increasing values of the independent variable while a negative coefficient indicates that gate violations decreased with increasing values of the variable. The coefficients in the model were statistically tested using a student s t-test to assess if they were different from zero (see Montgomery and Runger, 2007 for t-test details). Variable Period N_Opp TABLE 3 ESTIMATED POISSON MODEL FOR COUNT OF GATE VIOLATIONS PER TRAIN CROSSING EVENT Brief description/coding Indicator variable for time period; 0 if before educational activity, 1 if after educational activity Count of opportunities for non-motorists to engage in gate violations during a train crossing event (0, 1, 2, ) Estimated coefficient t- statistic P- value Variable Mean Marginal value -0.92-2.55 0.01 0.49-0.37 0.80 3.88 0.00 1.29 0.32 NM_Traffic Non-motorist traffic (1, 2, 3, ) -0.58-2.79 0.00 1.26-0.23 T_Arrival N_Trains Elapsed time between onset of flashing lights and train arrival at the crossing (seconds) Number of passing trains during a crossing event (1, 2, 3, ) 0.01 1.41 0.16 52.05 0.00-1.36-1.84 0.06 1.13-0.54 Constant Constant in the model -0.07-0.08 0.93 - -0.03 Model summary statistics Number of observations 96 Rho squared 0.13 Chi squared 21.58 Chi squared p-value 0.00 Estimated coefficient for the variable Period was negative and statistically significant at the 95% confidence level showing that gate violations decreased in the period after the educational campaign. Marginal value for the variable Period showed that gate violations reduced by 0.37 violations per train crossing event during the week after the educational campaign. The coefficient for variable N_Opp, representing opportunities available to non-motorists to engage in gate violations, was positive and statistically significant showing that greater opportunities were accompanied by higher counts of gate violations. The (2) (5) (6) - 13 -

coefficient for non-motorist traffic (NM_Traffic) was also statistically significant but the negative sign indicated that higher traffic was associated with lower counts of gate violations. This may be due to tendencies of non-motorists to engage in unsafe manoeuvres when no one else or just a few non-motorists are around. The association of greater elapsed time between the onset of flashing lights and train arrival at the crossing (T_Arrival) was investigated to assess the potential for ITS technologies for reducing gate violations; while the estimated coefficient was positive, and it was statistically not significant at the 95% confidence level. The variable representing the number of trains during an event (N_Trains) was negatively associated with counts of gate violations but this variable was also statistically not significant at the 95% level. The constant in the model captured the effects of variables not included in the model specification; however, it is statistically not significant at the 95% level. Other variables available in the database were tried in the model specification but their inclusion did not improve the model. In summary, modelling results showed that after accounting for opportunities, counts of gate violations per train crossing event reduced in the period after the educational campaign were undertaken at the HRGC; this is the main finding from the undertaken research. The next section presents research conclusions including a discussion on the potential to use ITS technologies for non-motorist safety improvement at gated HRGCs. V. CONCLUSIONS AND ITS DISCUSSION This study focused on evaluating the effects of a short-term educational campaign in improving HRGC safety for nonmotorists. The conclusion from this research is that the educational campaign was effective in improving non-motorists safety at the HRGC. Many agencies are hesitant to increase enforcement due to budget constraints or implement costly safetyenhancing engineering measures (e.g., construction of grade-separated crossings). This study presents evidence that agencies may rely on education to improve non-motorist safety in case budgetary or other considerations make enforcement or engineering measures less appealing. The availability of educational materials from Operation Lifesaver makes the process more expedient. Short educational campaigns, such as the one evaluated in this study, hold promise in improving safety at HRGCs when activities such as parades, marches, or gatherings are planned in proximity of HRGCs. To a significant extent, the effectiveness of ITS technologies in reducing gate violations at HRGCs by non-motorists depends on detecting trains, estimating train speed and acceleration/deceleration coupled with appropriate logic and algorithms for timing HRGC warning devices. Excessively long warning times may theoretically increase violations as users get tired of waiting for a train to arrive at the crossing and ITS technologies can potentially improve the situation by reducing unreasonably long warning times. However, this study did not uncover statistical evidence related to the relationship between train warning times and gate violations by non-motorists at HRGCs. Limitations of this study include the use of a single HRGC and a relatively small sample of observed non-motorists. Wider geographic coverage and larger sample sizes may reveal that the number of gate violations at gated HRGCs increases with longer warning times. Therefore, a study with broad geographic coverage with multiple sites is recommended for the future. REFERENCES [1] B. L. Bowman, K. Stinson, and C. Colson. Plan of action to reduce vehicle-train crashes in Alabama. Transportation Research Record 1648, Transportation Research Board, Washington, D.C., pp. 8-18, 1998. [2] Federal Railroad Administration, US Department of Transportation. Guidance on pedestrian crossing safety at or near passenger stations. April 2012. Available at: http://www.fra.dot.gov/downloads/safety/pedestriangradexingguidancedraft.pdf; accessed July 9, 2012. [3] Federal Railroad Administration, US Department of Transportation. Railroad safety statistics, 2010 preliminary annual report, 2012. Available at: http://safetydata.fra.dot.gov/officeofsafety/publicsite/prelim.aspx; accessed July 20, 2012. [4] S. M. Horton. Public education and enforcement research study Macomb, Illinois, analysis. Final report to FRA, DOT/FRA/ORG- 11/07, March 2011. [5] B. Lobb, N. Harre, and N. Terry. An evaluation of four types of railway pedestrian crossing safety interventions. Accident Analysis & Prevention, vol. 35, iss. 4, 2pp. 487-494, 2003. [6] B. Lobb, N. Harre, and T. Suddendorf. An evaluation of a suburban railway pedestrian crossing safety programme. Accident Analysis and Prevention, vol. 33, iss. 2, pp. 157-165, March 2001. [7] S. C. Mok and I. Savage. Why has safety improved at rail-highway grade crossings? Risk Analysis, Vol. 25 (4), August 2005, p. 867-881. [8] D. C. Montgomery and G. C. Runger. Applied statistics and probability for engineers. Fourth edition, John Wiley and Sons, New Jersey, 2007. [9] S. H. Richards and K. W. Heathington. Motorist understanding of railroad-highway grade crossing traffic control devices and associated traffic laws. Transportation Research Record 1160, Transportation Research Board, Washington, D.C., 1988, p. 52-59. [10] I. Savage. Does public education improve rail-highway crossing safety? 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http://www.tc.gc.ca/media/documents/railsafety/pedestriansafety-publications.pdf; accessed July 24, 2012. [13] S. Washington, M. Karlaftis, and F. Mannering. Statistical and econometric methods for transportation data analysis, 2nd ed., Chapman and Hall/CRC, 2011. [14] B. H. Tustin, H. Richards, H. McGee, and R. Patterson. Railroad-highway grade crossing Handbook, 2 nd Ed., FHWA TS-86-215, 1986. [15] American Railway Engineering and Maintenance-of-way Association (AREMA). Recommended instructions for determining warning time and calculating minimum approach distance for highway-rail grade crossing warning systems. AREMA Signal Manual, Part 3 3.3.10. Landover, Maryland, 2000. [16] R. Cho, and L. Rilett. Forecasting train travel times at at-grade crossings. Transportation Research Record 1844, Transportation Research Board, Washington, D.C., pp. 94-102, 2003. [17] R. Estes and L. Rilett. Advanced prediction of train arrival and crossing times at highway-rail grade crossings. Transportation Research Record 1708, Transportation Research Board, Washington, D.C., pp. 68-76, 1780. Aemal Khattak is Associate Professor at the University of Nebraska-Lincoln, USA. He obtained his Ph.D. from North Carolina State University in 1999 in the field of civil engineering. Dr. Khattak conducts research in the general area of highway safety with emphasis on highway-rail crossings. Zheng Luo is Civil Technologist at Scheffer Andrew Ltd., Canada. He obtained his Ph.D. from the University of Nebraska-Lincoln, USA in 2012 in civil engineering. Dr. Luo conducts traffic impact studies and transportation planning at his present position. - 15 -