Law enforcement agencies have begun utilizing traffic and crash data to improve traffic law enforcement delivery. However, many agencies often do not have the resources or expertise to harness fully the benefits this ...Law enforcement agencies have begun utilizing traffic and crash data to improve traffic law enforcement delivery. However, many agencies often do not have the resources or expertise to harness fully the benefits this data offers. A free to use, scalable traffic crash hot spot detection tool was developed to aid law enforcement agency decision makers, statewide to the local municipality level. The tool was developed to identify crash hot spots algorithmically with </span><span style="font-family:Verdana;">a range of customizable parameters based on location, date and time, and</span><span style="font-family:Verdana;"> crash factors, enabling quick, dynamic queries. These capabilities provide the ability for law enforcement agencies to conduct “what if” analyses and make data-driven allocation decisions, placing officer resources where they are most needed. The two-step algorithm first identifies potential hot spots based on </span><span style="font-family:Verdana;">crash density and then ranks each hot spot using a standardized z-score </span><span style="font-family:Verdana;">measure of relative significance. To test the viability of the tool, a pilot was conducted identifying 27 hot spots across Wisconsin where targeted enforcement was then deployed. Despite officer skepticism, results from the pilot found officers at sites targeted for speeding and seatbelt violations were nearly twice as likely to initiate traffic stops compared to non-targeted hot spots. Empirical Bayes before-and-after crash analyses found fatal and injury crashes reduced significantly by nearly 11% during the months with targeted enforcement, while property damage crashes and total crashes were unchanged. Overall, the results show the algorithm can identify hotspots where, coupled with targeted enforcement, traffic safety improvements can be made.展开更多
Air traffic is a typical complex system, in which movements of traffic components （pilots, controllers, equipment, and environment）, especially airport arrival and departure traffic, form complicated spatial and tem...Air traffic is a typical complex system, in which movements of traffic components （pilots, controllers, equipment, and environment）, especially airport arrival and departure traffic, form complicated spatial and temporal dynamics. The fluctuations of airport arrival and departure traffic are studied from the point of view of networks as the special correlation between different airports. Our collected flow volume data on the time-dependent activity of US airport arrival and departure traffic indicate that the coupling between the average flux and the fluctuation of an individual airport obeys a certain scaling law with a wide variety of scaling exponents between 1/2 and 1. These scaling phenomena can explain the interaction between the airport internal dynamics （e.g. queuing at airports, a ground delay program and following flying traffic） and a change in the external （network-wide） traffic demand （e.g. an increase in traffic during peak hours every day）, allowing us to further understand the mechanisms governing the collective behaviour of the transportation system. We separate internal dynamics from external fluctuations using a scaling law which is helpful for us to systematically determine the origin of fluctuations in airport arrival and departure traffic, uncovering the collective dynamics. Hot spot features are observed in airport traffic data as the dynamical inhomogeneity in the fluxes of individual airports. The intrinsic characteristics of airport arrival and departure traffic under severe weather is discussed as well.展开更多
The general objective of this research is to determine how to use the spatial analysis of traffic accidents in Medina Menorah City through geographic information systems. This research aimed to identify, locate and de...The general objective of this research is to determine how to use the spatial analysis of traffic accidents in Medina Menorah City through geographic information systems. This research aimed to identify, locate and define the sites where traffic accidents are concentrated and determine the need to apply specific safety standards to reduce accidents and identify their causes thereof. This current research applied the analytical descriptive approach for its relevance with this specific research. This research collected traffic accidents data from the Ministry of the Interior, Department of General Traffic. That data captured the hotspots accidents in Medina Menorah City. Some of the most important results of the study are as follows: many roads were selected as High Accident Location HAL, such as Central Ring Roads, King Faisal bin Abdul-Aziz Road, Prince Abdul Majid bin Abdul-Aziz Road, and King Abdulla bin Abdel-Aziz Road. The high-speed roads are heavily linked to the massive increase of traffic accident rates, and the increase in the street section length led to the soaring number of total accidents. The study recommended performing more studies and different highway safety studies to identify and locate accident patterns on road networks. Due to the fact that the accidents concentration is intensely focused on Medina City center and Prophet’s Mosque, it is a must to increase the number of public transportations to and from Prophet’s Mosque, particularly during the Hajj period, because of the fact that the visitors of Prophet’s Mosque is on the increase during the said period. This study can be applied in other cities because knowing the locations of traffic crash hotspots can provide us with valuable insights into the causes of accidents and this knowledge helps decision-makers to better assess the risk associated with accidents.展开更多
文摘Law enforcement agencies have begun utilizing traffic and crash data to improve traffic law enforcement delivery. However, many agencies often do not have the resources or expertise to harness fully the benefits this data offers. A free to use, scalable traffic crash hot spot detection tool was developed to aid law enforcement agency decision makers, statewide to the local municipality level. The tool was developed to identify crash hot spots algorithmically with </span><span style="font-family:Verdana;">a range of customizable parameters based on location, date and time, and</span><span style="font-family:Verdana;"> crash factors, enabling quick, dynamic queries. These capabilities provide the ability for law enforcement agencies to conduct “what if” analyses and make data-driven allocation decisions, placing officer resources where they are most needed. The two-step algorithm first identifies potential hot spots based on </span><span style="font-family:Verdana;">crash density and then ranks each hot spot using a standardized z-score </span><span style="font-family:Verdana;">measure of relative significance. To test the viability of the tool, a pilot was conducted identifying 27 hot spots across Wisconsin where targeted enforcement was then deployed. Despite officer skepticism, results from the pilot found officers at sites targeted for speeding and seatbelt violations were nearly twice as likely to initiate traffic stops compared to non-targeted hot spots. Empirical Bayes before-and-after crash analyses found fatal and injury crashes reduced significantly by nearly 11% during the months with targeted enforcement, while property damage crashes and total crashes were unchanged. Overall, the results show the algorithm can identify hotspots where, coupled with targeted enforcement, traffic safety improvements can be made.
基金Project supported by the National Natural Science Foundation of China (Grant No. 61039001)
文摘Air traffic is a typical complex system, in which movements of traffic components （pilots, controllers, equipment, and environment）, especially airport arrival and departure traffic, form complicated spatial and temporal dynamics. The fluctuations of airport arrival and departure traffic are studied from the point of view of networks as the special correlation between different airports. Our collected flow volume data on the time-dependent activity of US airport arrival and departure traffic indicate that the coupling between the average flux and the fluctuation of an individual airport obeys a certain scaling law with a wide variety of scaling exponents between 1/2 and 1. These scaling phenomena can explain the interaction between the airport internal dynamics （e.g. queuing at airports, a ground delay program and following flying traffic） and a change in the external （network-wide） traffic demand （e.g. an increase in traffic during peak hours every day）, allowing us to further understand the mechanisms governing the collective behaviour of the transportation system. We separate internal dynamics from external fluctuations using a scaling law which is helpful for us to systematically determine the origin of fluctuations in airport arrival and departure traffic, uncovering the collective dynamics. Hot spot features are observed in airport traffic data as the dynamical inhomogeneity in the fluxes of individual airports. The intrinsic characteristics of airport arrival and departure traffic under severe weather is discussed as well.
文摘The general objective of this research is to determine how to use the spatial analysis of traffic accidents in Medina Menorah City through geographic information systems. This research aimed to identify, locate and define the sites where traffic accidents are concentrated and determine the need to apply specific safety standards to reduce accidents and identify their causes thereof. This current research applied the analytical descriptive approach for its relevance with this specific research. This research collected traffic accidents data from the Ministry of the Interior, Department of General Traffic. That data captured the hotspots accidents in Medina Menorah City. Some of the most important results of the study are as follows: many roads were selected as High Accident Location HAL, such as Central Ring Roads, King Faisal bin Abdul-Aziz Road, Prince Abdul Majid bin Abdul-Aziz Road, and King Abdulla bin Abdel-Aziz Road. The high-speed roads are heavily linked to the massive increase of traffic accident rates, and the increase in the street section length led to the soaring number of total accidents. The study recommended performing more studies and different highway safety studies to identify and locate accident patterns on road networks. Due to the fact that the accidents concentration is intensely focused on Medina City center and Prophet’s Mosque, it is a must to increase the number of public transportations to and from Prophet’s Mosque, particularly during the Hajj period, because of the fact that the visitors of Prophet’s Mosque is on the increase during the said period. This study can be applied in other cities because knowing the locations of traffic crash hotspots can provide us with valuable insights into the causes of accidents and this knowledge helps decision-makers to better assess the risk associated with accidents.
