Abstract

INDOT has utilized many new technologies to reduce travel delay, mitigate traffic congestion, and enhance public and employees’ safety. Many projects are underway, including re-timing of most existing traffic signals and pavement maintenance and rehabilitation. To evaluate signal system performance, prioritize alternatives of improvement, conduct before and after studies and investigate work zone safety, it requires a large amount of traffic data such as travel time, speed and delay. Issues, such as safety, efficiency and cost, may arise associated with the current methods. For the current manual or automatic data collection, it usually requires two operators, a driver and a recorder. The current methods also require much time for data processing and the resulting data files may not be reusable. With the global positioning system (GPS) technologies, it appears that traffic data collection can be performed more safely and efficiently. The objective of this study is to investigate use of the GPS technologies to improve efficiency of INDOT traffic data collection, to enhance field operation safety, and to reduce potential human errors. Primary emphasis is given to utilization of GPS receivers to acquire traffic data so as to generate a reusable data file. This kind of data files is essential for providing INDOT traffic engineers with consistent information for assessing the performance of signal systems. Secondary emphasis is given to use of the GPS data in specific transportation studies, such as travel time and delay studies, work zone studies, and congestion management. In this study, the GPS techniques were examined and the Trimble AgGPS 132 devices were tested using the precisely known geographic points. A computer program, GPS-Trek, was developed for data collection and data processing. A huge amount of data was collected over the selected routes. Based on the results of analysis and field tests, a summary of the major findings are presented below: The proposed GPS data collection system is inexpensive and cost-effective. It can improve the efficiency of traffic data collection, save manpower and enhance field operation safety. The AgGPS 132 receiver can provide traffic data of high accuracy and consistency. The field data may exhibit gaps in “deep” urban canyons. However, through interpolation and proper data screening these disadvantages may be limited. The removal of Selective Availability from the GPS signals further improves the accuracy of GPS data. It was shown that the relative positional accuracy using a DGPS service is well below the 0.5 meter level. The GPS-Trek consists of two components, one for data collection and the other for data processing. The program is free of personal interpretation during data collection and provides a consistent system of analysis. The resulting data files are reusable. The data file can be easily exported to Microsoft® Excel, allowing traffic engineers to utilize their own experience and judgment for data analysis and specific transportation studies. A modified equation is presented to estimate the sample size requirements for field data collection using GPS devices. It was shown that in the filed tests, the modified equation produced a realistic estimate of the minimum sample size. Generally, a minimum of three initial test runs should be performed. If possible, five initial test runs for 90% confidence or six for 95% confidence are required to reduce the potential errors. The proposed system can be readily used for various transportation studies. This system bases its procedures on the existing INDOT highway digital map, resulting in a great saving in terms of manpower and time for creating a high resolution base map. The system also allows us to mark any critical points during data collection or data processing. As a result, it is possible for us to provide graphical reports, such as vehicle trajectory and speed profile that are required in the Manual of Transportation Engineering Studies and get a full picture of traveling situation on the test route. Also, this system can be used for measuring congestion on a system level.

Abstract

ualmente existem muitas fontes de dados oriundos de movimentação de pessoas e veículos. Deduzir o comportamento do tráfego a partir desses dados pode ser de grande utilidade para a gestão do tráfego. Entretanto, os métodos existentes podem não abordar todas as dimensões referentes ao comportamento do tráfego, em especial sob efeito de adversidades, como por exemplo - eventos climáticos. O objetivo dessa dissertação é apresentar uma combinação de métodos estatísticos e de análise espacial, para uso na compreensão dos padrões temporais e espaciais de variabilidade das velocidades, em condições normais e sob o impacto da precipitação. O método consiste na mineração de dados de micro trechos de deslocamento (microviagens), através da combinação de estatísticas descritivas, árvores de regressão CHAID (Chi square automatic interaction detection) e análises espaciais (Krigagem). Para a validação do método foram utilizados dados obtidos, durante março de 2014, de operação da frota de ônibus municipal da cidade de São Paulo, fornecidos pela São Paulo Transportes (SPTrans). Os dados de precipitação de chuva foram fornecidos pelo Instituto Nacional de Meteorologia (INMET). Os resultados demonstraram o impacto da precipitação sobre a variabilidade das velocidades por dia da semana e hora do dia. Adicionalmente, com base nos conjuntos de dados classificados via CHAID, foram produzidas representações da distribuição das velocidades sobre a superfície através uso da Krigagem. Observou-se uma consistente queda das velocidades no período vespertino, principalmente entre 16:00 e 19:00 horas, frequentemente agravados pela presença de precipitação. Foram selecionados casos de alta variabilidade, a partir da técnica CHAID, que foram analisados espacialmente. Os efeitos da queda da variabilidade foram observados geograficamente, demonstrando que a infraestrutura de transporte é um importante fator em sua caracterização. O estudo demonstra a viabilidade desta abordagem e a importância de incorporar a geografia no entendimento da variabilidade das velocidades em geral. Nowadays there is a wide number of movement data sources which can be useful in transportation management. However, the existing methods may not cover all traffic dimensions, especially under inclement weather. The objective of this work is presenting a combination of statistical and spatial methods which allows to understand space and time speed patterns, considering regular operation conditions in comparison with rainfall events. The method consists in sequential bus tracking data segments (micro travel) mining by the combination of descriptive statistics, CHAID (Chi square automatic interaction detection) regression trees and spatial analysis (Kriging). To validate the method, São Paulo bus fleet tracking data provided by SPTrans, and rainfall precipitation, provided by INMET was combined and processed. The results presented rainfall as a relevant factor of speed variability by day of week and time of the day. A consistent speed decrease in the afternoon period, mainly between 4:00 PM and 7:00 PM, was observed. Such effects are more intense in face of rainfall precipitation. The highest variability cases obtained by CHAID classification was selected and spatially analyzed showing the transportation infrastructure is an important variability component. Such results show the importance of a geographic approach over speed variability.

Abstract

Traffic congestion and its associated environmental effects pose a significant problem for large cities. Consequently, promoting and investing in green travel modes such as cycling is high on the agenda for many transport authorities. In order to target investment in cycling infrastructure and improve the experience of cyclists on the road, it is important to know where they are. Unfortunately, investment in intelligent transportation systems over the years has mainly focussed on monitoring vehicular traffic, and comparatively little is known about where cyclists are on a day to day basis. In London, for example, there are a limited number of automatic cycle counters installed on the network, which provide only part of the picture. These are supplemented by surveys that are carried out infrequently. Activity tracking apps on smart phones and GPS devices such as Strava have become very popular over recent years. Their intended use is to track physical activity and monitor training. However, many people routinely use such apps to record their daily commutes by bicycle. At the aggregate level, these data provide a potentially rich source of information about the movement and behaviour of cyclists. Before such data can be relied upon, however, it is necessary to examine their representativeness and understand their potential biases. In this study, the flows obtained from Strava Metro (SM) are compared with those obtained during the 2013 London Cycle Census (LCC). A set of linear regression models are constructed to predict LCC flows using SM flows along with a number of dummy variables including road type, hour of day, day of week and presence/absence of cycle lane. Cross-validation is used to test the fitted models on unseen LCC sites. SM flows are found to be a statistically significant (p<0.0001) predictor of total flows as measured by the LCC and the models yield R squared statistics of ~0.7 before considering spatio-temporal variation. The initial results indicate that data collected using fitness tracking apps such as Strava are a promising data source for traffic managers. Future work will incorporate the spatio-temporal structure in the data to better account for the spatial and temporal variation in the ratio of SM flows to LCC flows.

Abstract

Private car mobility registers a high accident rate and, in addition, in 2014 in the EU-28, around 70% of the overall"br" CO2 emissions from transport were generated by road mode. As a result, a modal shift of at least a part of passenger"br" transport in urban areas, from private car to sustainable transport systems is desirable. Several policies have been"br" adopted in the EU in this direction. The SaveMyBike project regards the development of a rewarding system for"br" sustainable mobility based on an open source platform able to monitor systematically trips in the city, starting to"br" integrate the platform with a service to incentive private bike use by means of UHF-RFID systems, creating secure"br" areas for bike parking and finding stolen bicycles. The real testing application to the Livorno case study from the"br" beginning of 2018. Then, SaveMyBike project introduces four innovations: it’s, for the first time, an open source"br" rewarding platform, called GOOD_GO linked to an anti-theft system for private bikes, it is applied to a whole city"br" and it introduces a ‘financially hot system’ for municipalities able to find financial resource from bike services.

Abstract

Emerging driver assistance systems, such as look-ahead cruise controllers for heavy duty vehicles, require high precision digital maps. This contribution presents a road grade estimation algorithm for fusion of GPS and vehicle real-time sensor data, with measurements from previous runs over the same road segment. The resulting road grade estimate is thus enhanced using measurements from additional traversals of known roads. Distributed data fusion is utilized to ensure that the storage requirement of known roads does not increase when additional measurements are processed. The implemented algorithm, which is based on extended Kalman filtering and smoothing, is described in detail. Experiments on a Scania test vehicle show the advantages and some of the challenges with the proposed approach. QC 20120216

Abstract

The automatic generation of street networks is attracting the attention of research and industry communities in areas such as routable map generation. This paper presents a novel mechanism that focuses on the automatic detection of street elements such as traffic lights, street crossings and roundabouts which could be used to generate street maps and populate them with traffic influencing infrastructural elements such as traffic lights. In order to minimize the system requirements and simplify the data collection from many users with minimal impact for them, only traces of GPS data from a mobile device while driving are used. Speed and acceleration time series are derived from the GPS data. An outlier detection algorithm is used first in order to detect abnormal driving locations (which can be due to infrastructural elements or particular traffic conditions). Using deep learning, speed and acceleration patterns are automatically analyzed at each outlier in order to extract relevant features which are then classified into a traffic light, street crossing, urban roundabout or other element. The classification results are enhanced by adding the degree of atypicity for each point calculated as the percentage of times that a particular location is detected as an outlier in several drives. The proposed algorithm achieves a combined recall of 0.89 and a combined precision of 0.88 for classification. The research leading to these results has received funding from the “HERMES-SMART DRIVER” project TIN2013-46801-C4-2-R (MINECO), funded by the Spanish Agencia Estatal de Investigación (AEI), and the “ANALYTICS USING SENSOR DATA FOR FLATCITY” project TIN2016-77158-C4-1-R (MINECO/ ERDF, EU) funded by the Spanish Agencia Estatal de Investigación (AEI) and the European Regional Development Fund (ERDF). Document type: Article

Abstract

"jats:p"For a safe overtaking manoeuvre on two-lane highways, drivers need a sufficient sight distance along the roadway, but it depends on the traffic environment. To provide an adequate Passing Sight Distance (PSD), a number of models have been proposed since the initial model introduced by American Association of State Highways and Transport Officials (AASHTO) in 1954. It is a current design practice in Sri Lanka as well, however, not validated for local traffic conditions. This paper will present an alternative PSD model and an evaluation of AASHTO model for national highway design. Experiment was based on GPS data collection. In conclusion, PSD demand was satisfied by AASHTO PSD under mix traffic conditions, but not the safety concerns for speeds greater than 80 km/h, while alternative model successful for all speeds. Document type: Article

Abstract

One of the most difficult and expensive tasks in making noise pollution maps is the collection and processing of the data needed to create acoustic models. In the case of road traffic noise maps, obtaining speed data for light and heavy vehicles a problem that has usually been avoided by using a road's speed limit or by making assumptions based on experience from similar road types. Here global positioning systems-based techniques are applied for acquiring vehicle speed data and adapted to fulfill the requirements of noise prediction models. © 2009 Elsevier Ltd. All rights reserved.

Abstract

Since GPS has been made available for civil usage, satellite-based localization in open space has become a more and more common option for vehicular tracking and for a number of commercial applications. The accuracy of the localization results and the availability of the localization system are influenced by several factors, such as the characteristics of the devices used, the surrounding environment, and the distance from reference stations. The possibility of exploiting off-the-shelf GPS devices, in the contest of a composite multi-sensor localization, is currently being investigated within the framework of the ALARP project [9] , where it is required to accurately localize workers in railway worksites. This paper presents the results of an experimental campaign aimed at determining if, and under which conditions, low-cost GPS devices can be used in such a scenario. The evaluation is performed comparing data from low-cost GPS devices to data collected using a highly accurate reference system. The analysis permits to assess the feasibility of two different, very popular, commercial GPS devices for the ALARP requirements on localization.

Abstract

"jats:p"Due to increasing human population and the number of vehicles, road traffic accident has significant influence on human life and economic development. In the present study, road traffic accident data of three years (2015-2017) were obtained from Hosanna Town Traffic Police Department, and Hosanna Town Transport Authority in Ethiopia. The Global Position System was used to know X, Y coordinates of the accident locations. Global Position System point data and accident data were added to road network data using the ‘Joins and relates’ function in ArcGIS. The results of the study showed a total of 241 Road traffic accidents (RTAs) were occurred in the town from which about 208 victims occurred on people and 33 damaged properties. Based on severity and frequencies of RTAs top nine hot spot areas were identified which requires high attention to protect people and property from damage and loss. Thus, the government and other concerned stakeholders should provide public education and awareness creation to reduce risk of fatalities and property damage due to RTAs in Hosanna town. Document type: Article