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Architecture of cloud-aided safety-based route planning.  

Architecture of cloud-aided safety-based route planning.  

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This paper presents a safety-based route planner that exploits vehicle-to-cloud-to-vehicle (V2C2V) connectivity. Time and road risk index (RRI) are considered as metrics to be balanced based on user preference. To evaluate road segment risk, a road and accident database from the highway safety information system is mined with a hybrid neural networ...

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... Fig. 8 shows that the risk index increases almost linearly as road segment length increases with an approximate slope of 4.5 RRI per mile. Fig. 9 indicates that segments with more lanes tend to carry higher risk. This result assumes that the AADT per lane is fixed at a nominal value. More lanes thus correspond to more traffic and higher risk. Fig. 10 shows that as AADT per lane increases, the risk tends to first decrease and then increase between 8000 and 15 000. This is not surprising given that RRI in (10) is proportional to the probability of having an accident. As a result, more traffic may lead to more total accidents but lower probability of an accident for any particular ...
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... 10 shows that as AADT per lane increases, the risk tends to first decrease and then increase between 8000 and 15 000. This is not surprising given that RRI in (10) is proportional to the probability of having an accident. As a result, more traffic may lead to more total accidents but lower probability of an accident for any particular vehicle. Fig. 11 indicates that the wider the lane, the safer the road segment. Fig. 12 shows risk first decreases and then increases with curvature and Fig. 13 shows that higher slope leads to higher risk. Note that the model has been developed for homogeneous road segments, in which the number of lanes, lane width, speed limit, etc., do not change. ...
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... and then increase between 8000 and 15 000. This is not surprising given that RRI in (10) is proportional to the probability of having an accident. As a result, more traffic may lead to more total accidents but lower probability of an accident for any particular vehicle. Fig. 11 indicates that the wider the lane, the safer the road segment. Fig. 12 shows risk first decreases and then increases with curvature and Fig. 13 shows that higher slope leads to higher risk. Note that the model has been developed for homogeneous road segments, in which the number of lanes, lane width, speed limit, etc., do not change. The homogeneous road segments are atomic, i.e., any route is composed of ...
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... given that RRI in (10) is proportional to the probability of having an accident. As a result, more traffic may lead to more total accidents but lower probability of an accident for any particular vehicle. Fig. 11 indicates that the wider the lane, the safer the road segment. Fig. 12 shows risk first decreases and then increases with curvature and Fig. 13 shows that higher slope leads to higher risk. Note that the model has been developed for homogeneous road segments, in which the number of lanes, lane width, speed limit, etc., do not change. The homogeneous road segments are atomic, i.e., any route is composed of these segments. However, for the purpose of route planning, it is more ...
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... to a report from the NHTSA [25], the later hours of the weekend and late afternoon to evening on the weekdays tend to be the riskiest periods for driving. The HSIS accident database includes hour of the day and day of the week of each accident. The distribution of number of accidents ver- sus day of the week is illustrated in Fig. 15. As shown in Fig. 15, the number of accidents occurring during a week is evenly distributed except that Fridays and Sundays are a lit- tle above and below average, respectively. Fig. 16 illustrates the distribution of weighted number of accidents over time of day for weekdays and weekends. The weighted number of accidents is defined as ...
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... to a report from the NHTSA [25], the later hours of the weekend and late afternoon to evening on the weekdays tend to be the riskiest periods for driving. The HSIS accident database includes hour of the day and day of the week of each accident. The distribution of number of accidents ver- sus day of the week is illustrated in Fig. 15. As shown in Fig. 15, the number of accidents occurring during a week is evenly distributed except that Fridays and Sundays are a lit- tle above and below average, respectively. Fig. 16 illustrates the distribution of weighted number of accidents over time of day for weekdays and weekends. The weighted number of accidents is defined as the number of ...
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... database includes hour of the day and day of the week of each accident. The distribution of number of accidents ver- sus day of the week is illustrated in Fig. 15. As shown in Fig. 15, the number of accidents occurring during a week is evenly distributed except that Fridays and Sundays are a lit- tle above and below average, respectively. Fig. 16 illustrates the distribution of weighted number of accidents over time of day for weekdays and weekends. The weighted number of accidents is defined as the number of accidents divided by the AADT distribution over a given one hour time interval, as shown in Fig. 17. Each bin in Fig. 17 histogram corresponds to a 1-h period. For ...
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... that Fridays and Sundays are a lit- tle above and below average, respectively. Fig. 16 illustrates the distribution of weighted number of accidents over time of day for weekdays and weekends. The weighted number of accidents is defined as the number of accidents divided by the AADT distribution over a given one hour time interval, as shown in Fig. 17. Each bin in Fig. 17 histogram corresponds to a 1-h period. For example, the bin centered at 0.5 describes the period from 0:00 to 0:59 A.M. The figures reveal similar conclusions to these presented in [25]. For instance, accidents are most likely in late nights during the weekend. This fact can be explained by a considerable number of ...
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... Sundays are a lit- tle above and below average, respectively. Fig. 16 illustrates the distribution of weighted number of accidents over time of day for weekdays and weekends. The weighted number of accidents is defined as the number of accidents divided by the AADT distribution over a given one hour time interval, as shown in Fig. 17. Each bin in Fig. 17 histogram corresponds to a 1-h period. For example, the bin centered at 0.5 describes the period from 0:00 to 0:59 A.M. The figures reveal similar conclusions to these presented in [25]. For instance, accidents are most likely in late nights during the weekend. This fact can be explained by a considerable number of drivers in the late ...
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... the bin centered at 0.5 describes the period from 0:00 to 0:59 A.M. The figures reveal similar conclusions to these presented in [25]. For instance, accidents are most likely in late nights during the weekend. This fact can be explained by a considerable number of drivers in the late night of the weekend being fatigued or impaired. Based on Fig. 16, a correction factor can be defined as the ratio of the weighted number of accidents to the average. For instance, travel at 1:30 A.M. on Saturday will have a correction factor of 580/304 = 1.91, where 580 is the weighted number of accidents in the 1-2 A.M. period in the weekend and 304 is the average weighted number of hourly ...
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... planning is a network flow problem [31]. A road network can be modeled as a directed graph as shown in Fig. 19(a). Intersections and road segments are abstracted as nodes and edges, respectively, in this directed graph. A route planner generates an optimal route in the transporta- tion network based on specified cost functions. Traditional planners mainly consider time, distance, or fuel economy. Route planners have been applied to a variety of ...
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... this section, we consider a real-world route planning case study. As illustrated in the Google Maps snapshot in Fig. 18(a), our goal is to plan a route from Scioto Downs Inc, Ohio to Delaware, Ohio. To plan the route, we first abstract the road network into the graph as shown in Fig. 19(a). Nodes rep- resent intersections of main roads included in the database. For example, node 2 represents the intersection of route 23 and interstate 270. The goal is to ...
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... this section, we consider a real-world route planning case study. As illustrated in the Google Maps snapshot in Fig. 18(a), our goal is to plan a route from Scioto Downs Inc, Ohio to Delaware, Ohio. To plan the route, we first abstract the road network into the graph as shown in Fig. 19(a). Nodes rep- resent intersections of main roads included in the database. For example, node 2 represents the intersection of route 23 and interstate 270. The goal is to find a path from node 1 to node 29 with a minimum cost specified in (10). For each edge, we define a pair of metrics (t i,j , r i,j ), which are the traveling time and ...
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... α = 0, the user does not care about road risk and desires a time-optimal route. As expected, the CPLEX results match the Google Maps results as shown in Fig. 18(a). The optimal time route in terms of Fig. 19(a) When α = 0.2, the optimal route is 1-2-3-9-14-15-20-26- 28-29, as shown in Fig. 18(b). The expected traveling time is 44 min and the total risk index is 103.57. The final cost J = 44 + 0.2 × 103.57 = 64.71. This second route has 36% less risk than the first route but requires 2 additional ...
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... α = 0, the user does not care about road risk and desires a time-optimal route. As expected, the CPLEX results match the Google Maps results as shown in Fig. 18(a). The optimal time route in terms of Fig. 19(a) When α = 0.2, the optimal route is 1-2-3-9-14-15-20-26- 28-29, as shown in Fig. 18(b). The expected traveling time is 44 min and the total risk index is 103.57. The final cost J = 44 + 0.2 × 103.57 = 64.71. This second route has 36% less risk than the first route but requires 2 additional minutes of travel ...
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... α = 0, the user does not care about road risk and desires a time-optimal route. As expected, the CPLEX results match the Google Maps results as shown in Fig. 18(a). The optimal time route in terms of Fig. 19(a) When α = 0.2, the optimal route is 1-2-3-9-14-15-20-26- 28-29, as shown in Fig. 18(b). The expected traveling time is 44 min and the total risk index is 103.57. The final cost J = 44 + 0.2 × 103.57 = 64.71. This second route has 36% less risk than the first route but requires 2 additional minutes of travel ...
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... now change our travel time back to 04:30 P.M. on Thursday and suppose it is snowing in east Columbus as seen in the pink area of Fig. 19(b). As discussed in Section II, the RRIs in the affected roads are adjusted using a correction factor β = 452.68/396.77, where 452.68 and 396.77 are the average number of daily accidents in snowy days and overall average number of accidents, respectively. A snow cover can significantly impact travel time as well as risk. While attributes ...
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... impact travel time as well as risk. While attributes of a road such as grade and curvature would certainly factor into travel speed reduction, in this paper, we adopt a repre- sentative 20% travel time increase based on a report showing data indicating a 5%-19% speed reduction range in snow [43]. With the updated time, RRIs [in red in Fig. 19(b)] and weight α = 0.2, the optimal route generated with CPLEX changes to 1-2-3-9-14-18-24-25-27-29. The expected traveling time is 42 min and the total risk index is 161.89. The final cost is J = 42 + 161.89 × 0.2 = ...

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