Automatic Number Plate Recognition Cameras Research Articles

Dealing with latency effects in travel time prediction on motorways

Real-time traffic information is now a crucial part of operating a road network. The quality, accuracy and reliability of such information is critical to the road operators and users. Real-time travel time prediction methods using Automatic Number Plate Recognition cameras or Bluetooth/Wi-Fi readers that use matching algorithms to generate travel times in real-time can be vulnerable to an inherent latency issue. Measured travel times are based on vehicles that have already completed the journey and may not be representative for users about to commence that same journey. The aim of this research was to identify the latency in travel time prediction, quantify its effect, and develop a model to remove it. Datasets for the M50 motorway in Dublin, Ireland, were used to conduct the analysis. The results show that real-time travel times can be more accurately predicted when combined with historical travel time information. The approach was found to be valid and achievable and the developed tool can predict and inform both road operators and users during regular periods of congestion. The project also identified other data sources, such as real-time Automated Incident Detection (AID) loop data, incident and weather data, that can further enhance the predicted travel time calculation.

Analyzing passenger and freight vehicle movements from automatic-Number plate recognition camera data

Modern urban-transport planning requires evidence-based insights into current transport flows to better understand the needs and impacts of policymaking. Urban transport includes passenger and freight vehicles, which have different behavior, and the need for such a separation is often ignored in research and practice [1]. New digital data sources provide an opportunity to better understand urban transport and identify where policy interventions are required. We review the literature on digital counting techniques to monitor transport flows, including loops, Automatic-Number Plate Recognition (ANPR) cameras and floating car data. We further investigate the potential of ANPR cameras, which are widely deployed, and which can be augmented with vehicle category information. This article presents the methodology that we follow for transforming raw augmented ANPR camera data into practical knowledge for city planners. Our is aim is to provide a better understanding of passenger and freight vehicle movements and stops, identifying similarities and differences between vehicle categories. We demonstrate our methodology on a case study for the Mechelen-Willebroek district in Belgium, encompassing augmented data from 122 ANPR cameras for a period of two weeks. Additionally, we also look at the car-reduced zone and how time restrictions affect the different vehicle categories’ actions. The findings are validated with GPS data from heavy-good vehicles in the same period. The potential of augmented ANPR camera data and promising themes and applications of this data source are illustrated through the case study.

The Impact of Weather on Traffic Speed in Urban Area

The issue of the impact of weather conditions on trip speed of vehicles has been studied for a long time and it is still the subject of many scientific researches. The impact of atmospheric conditions on the speed with which drivers drive their vehicles seems to be obvious. Good weather conditions, sunny weather with good visibility surely provokes higher speed while rainfall, wind and visibility limitations will force drivers to reduce speed, drive with more concentration, and be more cautious. In the light of emerging new possibilities of acquiring data on vehicle speed, especially travel speed indicating the speed of trip between designated speed measurement points using modern measurement technology new researches are possible. Increasingly in the ITS systems being implemented mainly in urban areas, ANPR (Automatic Number Plate Recognition) cameras are one of the ITS element. These cameras make it possible to determine the real time of appearance of the vehicle in the register area of the camera, thus determining both the matrix of trips between each two observation points (ANPR cameras), but also the vehicle’s ride time between successive cameras, and thus the speed of trip. In conjunction with data on weather conditions registered by several dedicated weather stations, they allow to significantly increase the research sample in terms of vehicle speed, and thus to obtain a statistically more confident inference. Thus, it gives a chance for further analysis regarding the impact of atmospheric conditions on road traffic conditions, including on travel speed and capacity of transport infrastructure. The article will present the results of empirical analyses to determine the impact of weather conditions (including temperature, ice thickness, air pressure and precipitation) on the vehicle trip speed.

Using cameras of automatic number plate recognition system for seat belt enforcement a case study of Sanliurfa (Turkey)

Purpose The purpose of this paper is to quantify the effect of the enforcement, which was carried out with ANPRs, on seat belt use. Though the Seat belt Act was enacted in 1992, it did not lead to an expected increase in seat belt use in Turkey including Sanliurfa, which is one of the immense provinces with a population of over 2m. The Sanliurfa Police Department set in an enforcement campaign, in which automatic number plate recognition (ANPR) cameras were used to facilitate an increment in using seat belts in the city center. Under the police leadership, seat belt use enforcement campaign was hugely publicized and sustained throughout the city. Design/methodology/approach The ANPRs did not have a feature to detect seat belt wearing automatically. Thus, this study tested whether automated plate recognition cameras have a deterrence effect on seat belt usage. To assess the efficacy of this enforcement project, the authors employed a pre/post-implementation design. For this study, the records of the 11 ANPR camera sites, 2 non-camera sites and 2 control sites were utilized. Findings The results of this study revealed that the seat belt use rate was around 8 percent, before camera enforcement in Sanliurfa. Overall increases were 12 percent during the warning period, 60 percent for the beginning period and 78 percent three months after enforcement began at camera sites. One-way ANOVA results suggested the differences between means of seat belt use counts were statistically significant F (3, 61,596)=15,456, p=0.000. Research limitations/implications The findings suggest that there are several reasons for the substantial increase in the seat belt use rate. The first reason for the success of the cameras was their deterrent effect on the drivers, because the drivers were aware that the traffic offense had become readily observable via camera detection in the intersections, and the drivers did not want to be penalized. Second, it is considered that a well-organized publicity of the cameras made a significant contribution to the effectiveness of the enforcement by increasing perceived detection risk. Finally, it is considered that the reason behind the sudden increase in seat belt use was the red-light cameras that had been already in use in Sanliurfa. Namely, the experience of the drivers about camera enforcement gave rise to the rapid decrease in seat belt violation rate in the warning period. Practical implications Using cameras (automatic or not) for seat belt enforcement and publicizing this enforcement can help to save resources and lives. Originality/value This study found a lot of news about similar enforcement on the internet, but no study was found in the literature that reveals if the enforcement can produce an effective result. Thus, this is the first study in Turkey, may be in the world, that evaluated if cameras of the ANPRS can generate effective seat belt enforcement. Furthermore, the study betokened that traffic violations, which cannot be automatically detected by cameras such as cell phone use and smoking in a vehicle can be effectively enforced by non-automatic cameras. Therefore, we believe that the study will contribute policing and the traffic safety literature.

Tracking Police Responses to \u201cHot\u201d Vehicle Alerts: Automatic Number Plate Recognition and the Cambridge Crime Harm Index

Research QuestionAre selective decisions to dispatch police cars for interception of vehicles identified by ‘live alerts’ from fixed cameras using an Automatic Number Plate Recognition (ANPR) database associated with the highest levels of harm across all live alerts?DataThis study examined 70.3 million vehicle registration marks read by Fixed ANPR cameras in the West Midlands Police area in April 2015, from which 12,581 live alerts were generated with many repeats, for 1488 unique vehicles identified. A random sample of 210 of these unique alerts was drawn for detailed analysis.MethodsCambridge Crime Harm Index values were applied to each of the 210 cases in the sample, with mean values computed and contrasted for alerts generating dispatched responses vs. no dispatch, interceptions vs. no interceptions and consequences imposed on drivers vs. no consequences.FindingsThe mean Cambridge Crime Harm Index (CCHI) value of the alerts leading to a dispatched response was 58% lower (59 CCHI days vs. 141) than the value of alerts not leading to a dispatch. The harm level of alert dispatches leading to interception was 57% lower (67 vs. 107 CCHI days) than those not intercepted. The harm level of interceptions leading to consequences was 57% lower (46 vs. 106 CCHI days).ConclusionsWhile resource limitations may affect these outcomes, there is great potential value in creating an automated CCHI calculator to inform the professional judgment of all police professionals involved in screening and responding to live alerts from ANPR.

Investigations of Section Speed on Rural Roads in Podlaskie Voivodeship

Excessive speed is one of the most important factors considered in road safety and not only affects the severity of a crash but is also related to the risk of being involved in a crash. In Poland the problem of speeding drivers is widely common. Properly recognized and defined drivers behaviour is the base for any effective activities taken towards road safety improvements. Effective enforcement of speed limits especially on rural road plays an important role but conducted speed investigations basically focus on spot speed omitting travel speed on longer sections of roads which can better reflect driver’s behaviour. Possible solutions for rural roads are limited to administrative means of speed limitations, installations of speed cameras and police enforcement. However due to their limited proved effectiveness new solutions are still being sought. High expectations are associated with the sectional speed system that has recently been introduced in Poland and covered a number of national road sections. The aim of this paper is to investigate section speed on chosen regional and district roads located in Podlaskie Voivodeship. Test sections included 19 road segments varied in terms of functional and geometric characteristics. Speed measurements on regional and district roads were performed with the use of a set of two ANPR (Automatic Number Plate Recognition) cameras. Conducted research allowed to compare driver’s behaviour in terms of travel speed depending on roads’ functional classification as well as to evaluate the influence of chosen geometric parameters on average section speed.

Reducing congestion during road works through travel demand management

Travel demand management research indicates that a quarter of drivers in London are willing and are able to change their behaviour when provided with persuasive travel advice. Enhanced information and travel advice about road works at a site in London in 2015 were communicated to the public, to mitigate congestion and to reduce customer impact. Data from automatic number plate recognition cameras from a neutral period were compared with data from the first week of road works to determine the rate of change in drivers’ behaviour. The same comparison was carried out for a control site without enhanced communications, and the difference between the rates of change was calculated to determine the effect of better communications. The data indicated that around 14% of frequent drivers were seen to have changed their time of travel or route through the road works, as a result of better communications. The monetised social benefit of that behaviour change set against the cost of enhanced communications generated a benefit:cost ratio of more than 4:1. Customer surveys indicated that the reputation of the highway authority was also upheld through the work periods.

How tube strikes affect macroscopic and link travel times in London

The purpose of this study was to investigate the impact of the five strikes on the London Underground (metro) rail system, which occurred in 2009 and 2010, on macroscopic and road link travel times. A consequence of these strikes was an increase in road traffic flows above usual levels. This provides an opportunity to observe the operation of the road network under unusually high flows. The first objective involves the examination of strike effects on inbound (IT) and outbound traffic (OT) within central, inner and outer London. Travel time data obtained from automatic number plate recognition cameras are used within the first part of the analysis. The second more detailed objective was to investigate in spatio-temporal effects on travel times on five road links. Correlation analyses and general linear models are developed using both traffic flow and travel time data. According to the results of the study, the morning IT had approximately twice as much delay as the OT. Central London experienced the highest delays, followed by inner and outer London. As would be expected, the unique full-day strike in 2009 yielded the worst impact on the network with the highest percentage increase in total travel time (60%) occurring during the morning peak in the IT in inner London. The results from the link-level analysis showed statistical significance amongst the examined links indicating heterogeneous effects from one link to another. It was also found that travel time changes may be more effectively captured through time-of-day terms compared to hourly traffic flows.

Effects of Tube Strikes on Journey Times in Transport Network of London

The goal of this study was to investigate the impact of five underground strikes on journey times in London's transport network during 2009 and 2010. The main data source for this study was automatic number plate recognition cameras, which were installed on the entrances and exits of 670 travel links that covered the vast majority of the network and were equivalent to a total length of 1,740 km. The determination of spatio-temporal differences of strike effects between the first and the remaining strike days, the identification of changes in departure and arrival times, and the estimation of travel time delays within central, inner, and outer London, as well as between inbound and outbound traffic, were the main objectives of the study. The total travel time within the examined areas, the excess delay, and the corresponding percentage difference in journey times were the main performance measurements used. The most significant results showed that the second day of strikes resulted in significant delays as opposed to the first strike days. The peaks elongated by approximately 45 to 60 min, while the unique full-day strike had the highest percentage increase in travel times, especially during the evening period (74%). Central London was generally affected the most, especially during the morning peak, which experienced an average increase in travel times of 35%, while Central London also had the highest percentage of negatively affected links (80%). The inbound traffic experienced, on average, high delays during the morning peak; the outbound traffic yielded greater delays during the evening period.

Modeling of Travel Time Variations on Urban Links in London

An econometric framework was developed to combine data from various sources to identify the key factors contributing to travel time variations in Central London. Nonlinear latent variable regression models that explicitly accounted for measurement errors in the data were developed to combine data extracted from automatic number plate recognition cameras and automatic traffic counters. This procedure significantly differed from previous research in this area that was based primarily on traffic flow data and ignored measurement errors. The results indicate that nonlinear latent variable regression models can effectively explain travel time variations on a regular day by using variables related to vehicle type, traffic density, and traffic composition. Test results indicate that the proposed framework for correcting measurement errors yields significant improvements over base models, where such errors are ignored. The findings from the study validate some key hypotheses regarding influences of various factors on speed of urban traffic streams and can serve as a tool for investigation of the causes of traffic congestion. The model framework is general enough for application in other cases in which traffic data have similar measurement errors.

A Fatal Motorcycle Accident, UK Road Traffic Legislation Since 1903 and Recent Changes in the Law

The risks of traveling by motorcycle are well recognized, both in terms of excess speed and relative lack of physical protection. Legislation since 1903 has been on-going to try to reduce the carnage on the roads and to recognize the financial problems that such a death leaves behind for the families of the victim. An overview of speed cameras currently in use is given with the more frequent use of ANPR (automatic number plate recognition) cameras for surveillance of known and suspected criminals, valid car insurance verification, current Ministry of Transport mechanical certificates and, increasingly, for with average speed calculation between camera sites. The latter resulting in the surprising and unexpected penalty notice in the post often many days after the incident, though to be legal in England and Wales, it must be served with 14 days of the infraction. Discussed in this case, and of considerable interest, is the hypothesis that a full-face helmet for a motorcyclist may be potentially more dangerous in a crash, due the increased weight, inertia and pendulum effect result in an increased risk of fatal basal skull ring fractures.

A Neural Network Approach to Motorway OD Matrix Estimation from Loop Counts

A method has been developed to estimate Origin Destination (OD) matrices using a neural network (NN) model and loop traffic data collected from a UK motorway site (M42) as the primary input. The estimated ODs were validated against matched vehicle number plate data derived from the ANPR (Automatic Number Plate Recognition) cameras which were installed at all the slip roads between junctions 3a and 7 of the motorway. Key research questions were: whether it is realistic to use the full loop data, whether particular features of the data influenced modelling success, whether data transformation could improve modelling performance through variance stabilization and whether individual ODs should be estimated separately or simultaneously. The method has been shown to work well and the best results were obtained using a square root transformation of the training data and individual models for each OD.