mobile systems applied to traffic management and safety a state of the art

Abstract Mobile systems applied to traffic management and control and traffic safety have the potential to shape the future of road transportation. The following innovations, that will be deployed on a large scale, could reshape road traffic management practices: – the implementation of connected vehicles with global navigation satellite (GNSS) system receivers; – the autonomous car revolution; – the spreading of smartphone-based systems and the development of Mobile Cooperative Web 2.0 which is laying the base for future development of systems that will also incorporate connected and autonomous vehicles; – an increasing need for sustainability of transportation in terms of energy efficiency, traffic safety and environmental issues. This paper intends to provide a state of the art on current systems and an anticipation of how mobile systems applied to traffic management and safety could lead to a completely new transportation system in which safety and congestion issues are finally properly addressed

A Cooperative Intelligent Transportation System for Traffic Light Regulation Based on Mobile Devices as Floating Car Data (FCD)

This paper carries on an analysis in a simulation framework to establish numerically what would happen at the launch of the system and when the subscriber base increases. It shows that after around 35% of subscribers would have joined, the system would operate more democratically and very efficiently in traffic regulation by estimating the position of all vehicles in the network using the subscriber base as a sample for the whole population of cars. An algorithm is proposed for the optimization of an isolated traffic light and for an isolated corridor. Micro-simulation has been used to make confrontations and estimation, in the analyzed scenarios, between the proposed system (using active mobile phone as sensors) and fixed time signal settings based on standard Highway Capacity Manual (HCM) procedure.Numerical results are obtained that assess the proposed system from the point of view of both subscriber advantages and overall network travel time reduction depending on different subscriber rates in the driver population. Results have shown a great convenience of the system for low traffic flows and intersections not perfectly regulated. The proposed optimization algorithm can be extended to a whole network.This paper presents and analyzes a new Cooperative Intelligent Transportation System based on a simple concept: the use of information coming from the network of internet connected mobile devices to regulate traffic light systems. The new idea explored in this paper is a traffic light system with green cycles actuated by the information coming from mobile phone vehicle probes that would allow:1)       regulating traffic lights or offering information to drivers taking advantage of Floating Car Data (FCD);2)       convincing drivers to accept this system as beneficial and adopt it promptly and voluntarily becoming part of a successful cooperative system.The idea presented in this paper is conceptually very simple: drivers interested in the service would install a mobile phone application on mobile devices with GNSS capabilities (such as GPS or Galileo system). This would allow the traffic light regulation system to know the position of subscribers and regulate traffic lights according to this. The fast and successful launch of the system would be guaranteed by the fact that, in the launch phase, the first drivers using the system would be given green priority on other drivers

Systematic bias in transport model calibration arising from the variability of linear data projection

In transportation and traffic planning studies, accurate traffic data are required for reliable model calibration to accurately predict transportation system performance and ensure better traffic planning. However, it is impractical to gather data from an entire population for such estimations because the widely used loop detectors and other more advanced wireless sensors may be limited by various factors. Thus, making data inferences based on smaller populations is generally inevitable. Linear data projection is a commonly and intuitively adopted method for inferring population traffic characteristics. It projects a sample of observable traffic quantities such as traffic count based on a set of scaling factors. However, scaling factors are subject to different types of variability such as spatial variability. Models calibrated based on linearly projected data that do not account for variability may introduce a systematic bias into their parameters. Such a bias is surprisingly often ignored. This paper reveals the existence of a systematic bias in model calibration caused by variability in the linear data projection. A generalized multivariate polynomial model is applied to examine the effect of this variability on model parameters. Adjustment factors are derived and methods are proposed for detecting and removing the embedded systematic bias. A simulation is used to demonstrate the effectiveness of the proposed method. To illustrate the applicability of the method, case studies are conducted using real-world global positioning system data obtained from taxis. These data calibrate the Macroscopic Bureau of Public Road function for six 1 × 1 km regions in Hong Kong.postprin

Mobile Phone Data from GSM Networks for Traffic Parameter and Urban Spatial Pattern Assessment - A Review of Applications and Opportunities

The use of wireless location technology and mobile phone data appears to offer a broad range of new opportunities for sophisticated applications in traffic management and monitoring, particularly in the field of incident management. Indeed, due to the high market penetration of mobile phones, it allows the use of very detailed spatial data at lower costs than traditional data collection techniques. Albeit recent, the literature in the field is wide-ranging, although not adequately structured. The aim of this paper is to provide a systematic overview of the main studies and projects addressing the use of data derived from mobile phone networks to obtain location and traffic estimations of individuals, as a starting point for further research on incident and traffic management. The advantages and limitations of the process of retrieving location information and transportation parameters from cellular phones are also highlighted. The issues are presented by providing a description of the current background and data types retrievable from the GSM network. In addition to a literature review, the main findings on the so-called Current City project are presented. This is a test system in Amsterdam (The Netherlands) for the extraction of mobile phone data and for the analysis of the spatial network activity patterns. The main purpose of this project is to provide a full picture of the mobility and area consequences of an incident in near real time to create situation awareness. The first results from this project on how telecom data can be utilized for understanding individual presence and mobility in regular situations and during non-recurrent events where regular flows of people are disrupted by an incident are presented. Furthermore, various interesting studies and projects carried out so far in the field are analyzed, leading to the identification of important research issues related to the use of mobile phone data in transportation applications. Relevant issues concern, on the one hand, factors that influence accuracy, reliability, data quality and techniques used for validation, and on the other hand, the specific role of private mobile companies and transportation agencies.JRC.H.6-Digital Earth and Reference Dat

Detecting metro service disruptions via large-scale vehicle location data

Urban metro systems are often affected by disruptions such as infrastructure malfunctions, rolling stock breakdowns and accidents. The crucial prerequisite of any disruption analytics is to have accurate information about the location, occurrence time, duration and propagation of disruptions. To pursue this goal, we detect the abnormal deviations in trains’ headway relative to their regular services by using Gaussian mixture models. Our method is a unique contribution in the sense that it proposes a novel, probabilistic, unsupervised clustering framework and it can effectively detect any type of service interruptions, including minor delays of just a few minutes. In contrast to traditional manual inspections and other detection methods based on social media data or smart card data, which suffer from human errors, limited monitoring coverage, and potential bias, our approach uses information on train trajectories derived from automated vehicle location (train movement) data. As an important research output, this paper delivers innovative analyses of the propagation progress of disruptions along metro lines, which enables us to distinguish primary and secondary disruptions as well as effective recovery interventions performed by operators

Biased standard error estimations in transport model calibration due to heteroscedasticity arising from the variability of linear data projection

Reliable transport models calibrated from accurate traffic data are crucial for predicating transportation system performance and ensuring better traffic planning. However, due to the impracticability of collecting data from an entire population, methods of data inference such as the linear data projection are commonly adopted. A recent study has shown that systematic bias may be embedded in the parameters calibrated due to linearly projected data that do not account for scaling factor variability. Adjustment factors for reducing such biases in the calibrated parameters have been proposed for a generalized multivariate polynomial model. However, the effects of linear data projection on the dispersion of and confidence in the adjusted parameters have not been explored. Without appropriate statistics examining the statistical significance of the adjusted model, their validity in applications remains unknown and dubious. This study reveals that heteroscedasticity is inherently introduced by data projection with a varying scaling factor. Parameter standard errors that are estimated by linearly projected data without any appropriate treatments for non-homoscedasticity are definitely biased, and possibly above or below their true values. To ensure valid statistical tests of significance and prevent exposure to uninformed and unnecessary risk in applications, a generic analytical distribution-free (ADF) method and an equivalent scaling factor (ESF) method are proposed to adjust the parameter standard errors for a generalized multivariate polynomial model, based on the reported residual sum of squares. The ESF method transforms a transport model into a linear function of the scaling factor before calibration, which provides an alternative solution path for achieving unbiased parameter estimations. Simulation results demonstrate the robustness of the ESF method compared with the ADF method at high model nonlinearity. Case studies are conducted to illustrate the applicability of the ESF method for the parameter standard error estimations of six Macroscopic Bureau of Public Road functions, which are calibrated using real-world global positioning system data obtained from Hong Kong.postprin

Mobiiliverkkodatan käytön validointi lähtö-määränpää -matriisien luomisessa

The rapid development in telecommunication networks during last years has made it possible to study human travel behaviour effectively from mobile network data. The combination of passive and active signalling events gathered by mobile network operators allow analysing movements of people with full longitudinal and spatial coverage. Therefore, recent years have seen an increasing interest in utilizing mobile network data in transportation studies, as an alternative or a complementary data source for conventional transport data. This study validates the capability of mobile network data to produce long-distance origin-destination matrices in Finland. Features that are being validated include trip counts, seasonal trip count changes and modal split. As reference data sources of the study, the National Travel Survey 2016, HELMET-transport demand model (Transport model by HSL) and LAM-data (automated traffic census) are used. Validation is done by analysing correlations between mobile network data and the reference data sources. By being able to demonstrate the validity and reliability of mobile network data usage in producing origin-destination matrices, cost-effectiveness and more accurate methods to gather information from long-distance transportation can be provided for the field in general. The overall results of the study are in line with the few similar related studies that have been conducted. The thesis work suggests that mobile network data is capable of producing more reliable trip counts from sparsely populated areas than the National Travel Survey. In addition, it seems to be more capable of capturing the high summer peak in longdistance travelling in Finland. The results regarding modal split are promising, but more studies regarding the modal detection will be needed.Matkapuhelinverkkojen viime vuosien nopea kehitys on mahdollistanut yhä tarkemman matkapuhelinten solupaikannuksen. Teleoperaattoreiden keräämä passiivisten ja aktiivisten matkapuhelinverkon signaalihavaintojen yhdistelmä mahdollistaa ihmisten liikkumiskäyttäytymisen tutkimisen kattavasti sekä ajallisesti että alueellisesti. Viime aikoina matkapuhelinverkkodatan hyödyntäminen liikennetutkimuksissa on tästä syystä herättänyt kasvavaa kiinnostusta perinteisten tiedonkeruumenetelmien korvaajana ja täydentäjänä. Tämä tutkimus validoi mobiiliverkkodatan käyttöä lähtö-määränpää -matriisien luomisessa Suomen pitkän matkan liikenteessä. Validoitavia ominaisuuksia ovat matkamäärät, matkamäärien vuodenajoittainen vaihtelu ja matkojen kulkumuotojakauma. Referenssiaineistona työssä käytetään Suomen Henkilöliikennetutkimusta, HELMET-liikennemallia ja LAM-dataa. Validointi suoritetaan analysoimalla mobiiliverkkodatan ja referenssiaineistojen välisiä korrelaatioita. Osoittamalla mobiiliverkkodatan käytettävyys lähtö-määränpää matriisien luomisessa, liikennesuunnittelun kustannustehokkuutta ja keinoja tarkemman tiedon keräämiseen pitkämatkaisesta liikkumisesta voidaan edistää. Työn tulokset ovat linjassa aiemman tutkimuksen kanssa. Tulokset näyttävät mobiiliverkkodatan olevan kykenevä tuottamaan lähtö-määränpää -tietoa hajaasutusalueilta luotettavammin kuin Henkilöliikennetutkimus. Lisäksi, mobiiliverkkodata näyttää pystyvän observoimaan kesän lomakauden matkapiikin tarkemmin kuin Henkilöliikennetutkimus. Tulokset mobiiliverkkodatan kulkumuototunnistukseen ovat lupaavia, mutta lisää tutkimusta tarvitaan näiden havaintojen vahvistamiseen