Neighbouring Link Travel Time Inference Method Using Artificial Neural Network

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This paper presents a method for modelling relationship between road segments using feed forward back-propagation neural networks. Unlike most previous papers that focus on travel time estimation of a road based on its traffic information, we proposed the Neighbouring Link Inference Method (NLIM) that can infer travel time of a road segment (link) from travel time its neighbouring segments. It is valuable for links which do not have recent traffic information. The proposed method learns the relationship between travel time of a link and traffic parameters of its nearby links based on sparse historical travel time data. A travel time data outlier detection based on Gaussian mixture model is also proposed in order to reduce the noise of data before they are applied to build NLIM. Results show that the proposed method is capable of estimating the travel time on all traffic link categories. 75% of models can produce travel time data with mean absolute percentage error less than 22%. The proposed method performs better on major than minor links. Performance of the proposed method always dominates performance of traditional methods such as statistic-based and linear least square estimate methods

A simulation study of predicting real-time conflict-prone traffic conditions

Current approaches to estimate the probability of a traffic collision occurring in real-time primarily depend on comparing traffic conditions just prior to collisions with normal traffic conditions. Most studies acquire pre-collision traffic conditions by matching the collision time in the national crash database with the time in the traffic database. Since the reported collision time sometimes differs from the actual time, the matching method may result in traffic conditions not representative to pre-collision traffic dynamics. In this study, this is overcome through the use of highly disaggregated vehicle-based traffic data from a traffic micro-simulation (i.e. VISSIM) and the corresponding traffic conflicts data generated by the Surrogate Safety Assessment Model (SSAM). In particular, the idea is to use traffic conflicts as surrogate measures of traffic safety so that traffic collisions data are not needed. Three classifiers (i.e. Support Vector Machines, k-Nearest Neighbours and Random Forests) are then employed to examine the proposed idea. Substantial efforts are devoted to making the traffic simulation as representative to real-world as possible by employing data from a motorway section in England. Four temporally aggregated traffic datasets (i.e. 30-second, 1-minute, 3-minute and 5-minute) are examined. The main results demonstrate the viability of using traffic micro-simulation along with the SSAM for real-time conflicts prediction and the superiority of Random Forests with 5-minute temporal aggregation in the classification results. Attention should be however given to the calibration and validation of the simulation software so as to acquire more realistic traffic data resulting in more effective prediction of conflicts

Contributions to time series data mining departing from the problem of road travel time modeling

194 p.Bidaiarientzako Informazio Sistema Aurreratuak (BISA) errepideetan sensoreenbidez bildutako datuak jaso, prozesatu eta jakitera ematen dituzte,erabiltzailei haien bidaietan lagunduz eta ibilbidea hasi baino lehen eta bideanhartu beharreko erabakiak erraztuz [5]. Helburu honetarako, BISA sistemektrafiko ereduak beharrezkoak dituzte, bidaiarientzat baliagarriak izandaitezkeen trafiko aldagaiak deskribatu, simulatu eta iragartzeko balio duelako.Zehazki, kontutan hartu daitezkeen trafiko aldagai guztietatik (fluxua,errepidearen okupazioa, abiadurak, etab.) bidai denbora da erabiltzaileentzatintuitiboena eta ulerterrazena den aldagaia eta, beraz, BISA sistemetan garrantziberezia hartzen duena [6]. Bidai denbora, aurrez zehaztutako puntubatetik bestera joateko ibilgailu batek behar duen denborari deritzo.Bidai denboren eredugintzan bi problema nagusi bereizten dira: estimazioaeta iragarpena. Nahiz eta literaturan batzuetan bi kontzeptu hauek baliokidetzatjo, berez, bi problema bereizi dira, ezaugarri eta helburu ezberdinekin,eta teknika ezberdinak eskatzen dituztenak.Alde batetik, bidai denboren estimazioaren helburua iada amaitutakobidaietan ibilgailuak bataz beste zenbat denbora igaro duten kalkulatzeada. Horretarako, ibilbidean zehar jasotako trafikoari buruzko informazioaedo/eta bestelako datuak (eguraldia, egutegiko informazioa, etab.) erabildaitezke [1]. Estimazio metodo ezberdinak eskuragarri dauden datu motaeta kantitatearen araberara sailka daitezke eta, a posteriori motako balorazioakegiteko balio dute. Bestalde, bidai denboren iragarpena, orainean edoetorkizunean hasiko diren bidaien denborak kalkulatzean datza. Honetarako,iragarpena egiten den momentuan jasotako eta iraganeko trafikoari buruzkodatuak eta testuinguruko informazioa erabiltzen da [8].Ibilgailu kopuru eta auto-ilaren ugaritzeen ondorioz, bidai denboren estimazioeta predikzio onak lortzea geroz eta beharrezkoagoa da, trafikoarenkudeaketa egokia ahalbidetzen duelako. Hau ikusirik, azken urteetan eredumota ezberdin andana proposatu eta argitaratu dira. Nolanahi ere, literaturarenberrikuspen eta analisi sakon bat egin dugu tesi honen lehenengoatalean. Bertan, ondorioztatu ahal izan dugu proposatutako eredu guztiakez direla egokiak errepide sare, trafiko egoera eta datu mota guztiekin erabiltzeko.Izan ere, atera dugun ondorio nabariena, argitaratutako eredu askokez dituztela BISA sistemen eskakizun praktikoak betetzen, da. Lehenik etabehin, eredu asko errepide zati txikietan soilik aplika daitezke, eta ez dagoargi errepide sare guztira nola hedatu daitezkeen. Bestalde, eredu gehienekdatu mota bakarra erabiltzen dute eta errealitatean ohikoa da datu mota batekinbaina gehiagorekin lan egin behar izatea. Azkenik, pilaketa ez-ohikoenaurrean malgutasun mugatua izatea ere desabantaila nabari eta ohikoa da.Hau honela, eredu konbinatu edo hibridoak proposamen hauetatik guztietatiketorkizun handiena dutenak direla dirudi, patroi ezberdinetara moldatzekogaitasuna dutelako, eta eredu eta datu mota ezberdinak nahastekoaukera ematen dutelako.Tesi honetan, bidai denborak iragartzeko eredu hibrido edo konbinatuakhartuko ditugu abiapuntutzat. Zehazki, hasieran datuak antzekotasunarenarabera multzokatzen dituenetan jarriko dugu arreta. Metodo hauek, datuakmultzokatu ondoren, multzo bakoitzari bidai denborak iragartzeko eredu ezberdinbat aplikatzen diote, zehatzagoa eta patroi espezifiko horrentzat espresukieraikia.Eredu talde honen kasu berezi bat, datuen multzokatzea denbora serieentaldekatzearen bitartez egiten duena da. Denbora serieen taldekatzea (clustering-a ingelesez) datu mehatzaritzako gainbegiratu gabeko ataza bat da, nonhelburua, denbora serie multzo, edo beste era batera esanda, denbora seriedatu base bat emanik, serie hauek talde homogeneoetan banatzea den [3]. Xedea,beraz, talde bereko serieen antzekotasuna ahalik eta handiena izatea etaaldiz, talde ezberdinetako serieak ahalik eta desberdinenak izatea da. Trafikodatuetan eta bidai denboretan, portaera ezberdinetako egunak aurkitzea osoohikoa da (adib. asteguna eta asteburuak). Hau honela, egun osoan zeharjasotako bidai denborez osatutako serie bat izanik, metodo mota honek lehenik,dagokion egun mota identifikatuko luke eta ondoren iragarpenak egunmota horretarako bereziki eraikitako eredu batekin lortuko lituzke.Denbora serieen clustering-an oinarritutako eredu mota hau ez da ia inoizerabili literaturan eta, ondorioz, bere onurak eta desabantailak ez dira ondoaztertu orain arte. Honegatik, tesi honen bigarren kapituluan, eredugintzaprozeduaren hasieran egun mota ezberdinak identifikatzea bidai denboreniragarpenak lortzeko lagungarria ote den aztertu dugu, emaitza positiboaklortuz. Hala ere, praktikan, honelako eredu konbinatuak eraikitzeak eta erabiltzeakzailtasun bat baino gehiago dakartza. Tesi honetan bi arazo nagusietanjarriko dugu arreta eta hauentzat soluzio bana proposatzea izango duguhelburu.Hasteko, denbora serieak multzokatzeko, erabaki ez tribial batzuk hartubehar dira, adibidez distantzia funtzio egoki bat aukeratzea. Literaturanbehin baino gehiagotan erakutsi da erabaki hau oso garrantzitsua dela etaasko baldintzatzen dituela lortuko diren emaitzak [7]. Trafikoko kasuan ere,hau honela dela demostratu dugu. Baina distantzia baten aukeraketa ez dabatere erraza. Azken urteotan hamaika distantzia ezberdin proposatu dituikerlari komunitateak denbora serieekin lan egiteko eta, dirudienez, datu basebakoitzaren ezaugarrien arabera, bat ala bestea izaten dela egokiena [3, 7].Guk dakigula, ez dago metodologia formalik erabiltzaileei aukeraketa hauegiten laguntzen dionik, ez batik bat denbora serieen clustering-aren testuinguruan.Metodologia ohikoena distantzia sorta bat probatzea eta lortutakoemaitzen arabera bat aukeratzea da. Zoritxarrez, distantzia batzuen kalkuluakonputazionalki oso garestia da, eta beraz, estrategia hau ez da batereeraginkorra praktikan.Ataza hau simplifikatzeko asmoarekin, tesiko hirugarren kapituluan etiketaanitzeko sailkatzaile bat (ingelesez multi-label classifier ) proposatzen dugudenbora serieen datu base bat multzokatzeko, distantzia egokiena modu automatikoanaukeratzen duena. Sailkatzaile hau eraikitzeko, hasteko, denboraserie datu base baten alderdi batzuk deskribatzeko ezaugarri sorta bat definitudugu. Besteak beste, datuetan dagoen zarata maila, autokorrelazio maila,serie atipikoen kopurua, periodizitatea eta beste hainbat ezaugarri neurtu etakuantifikatzeko metodoak proposatu ditugu. Ezaugarri hauek sailkatzaileakbehar duen input informazioa edo, bestela esanda, sailkatzailearen menpekoaldagaiak izango dira. Emaitza gisa, sailkatzaileak datu base batentzategokienak diren distantziak itzuliko dizkigu, kandidatu sorta batetik, noski.Sailkatzaile honen baliagarritasuna egiaztatzeko, esperimentu sorta zabalbat bideratu dugu, bai lan honetarako bereziki sortutako datu base sintetikoekineta bai UCR artxiboko [4] benetako datuak erabiliz. Lortutako emaitzapositiboak argi uzten dute proposatutako sailkatzaileak denbora serie batmultzokatzeko distantzia funtzio baten aukeraketa errazteko balio duela.Ekarpen hau azalduta, berriz bidai denboren iragarpenerako eredu kon-binatuetara itzuli eta bigarren problema bat identifikatzen dugu, tesiko bigarrenekarpen nagusira eramango gaituena. Gogoratu eredu konbinatu hauekhasiera batean datuak multzokatzen dituztela, clustering algoritmoak erabiliz.Talde bakoitzak patroi edo trafiko portaera ezberdin bat adieraziko du.Ondoren, talde bakoitzean iragarpenak egiteko, iragarpen eredu ezberdin bateraikiko dugu, soilik multzo horretako datu historikoak erabiliz. Gure kasuan,denbora serieen clustering-a aplikatu dugu eta beraz, egun mota ezberdinaklortuko ditugu. Ondoren, iragarpen berriak egin nahi ezkero, egun berri bathasten denean, zein multzokoa den asmatu beharko dugu, erabili behar duguneredua aukeratzeko.Ohartu, iragarpenak egiteko garaian, ez dugula egun osoko daturik izangoeskuragarri. Adibidez, goizeko hamarretan, eguerdiko hamabietan (2 ordugeroago) puntu batetik bestera joateko beharko dugun denbora iragarri nahibadugu, soilik egun horretan hamarrak arte jasotako informazioa izango dugueskuragarri, informazio historikoarekin batera, noski. Egoera honetan, egunhorretako informazio partzialarekin, seriearen lehen zatiarekin soilik, erabakibehar dugu zein multzotakoa den. Noski, ordurarte jasotako informazioa ezbada nahikoa adierazgarria, kalterako izan daiteke multzo eta eredu zehatzbat aukeratzea, eta ziurrenik hobe izango da eredu orokorrago bat erabiltzea,datu historiko guztiekin eraikia. Finean, egun berriak ahal bezain prontomultzo batera esleitu nahi ditugu, baina esleipen hauetan ahal bezain erroregutxien egin nahi dugu.Logikoa da pentsatzea esleipenak geroz eta lehenago eginez akatsak egitekoaukera handiagoa dela. Hau honela, helburua esleipenak ahal bezain azkaregitea da, baina zehaztasun maila onargarri bat bermatuz. Denbora serieenmehatzaritzan problema honi denbora serieen sailkapen goiztiarra (ingelesezearly classification of time series) deritzo [10].Denbora serieen sailkapena (ingelesez time series classification) [9, 10] datumehatzaritzako gainbegiratutako problema aski ezaguna da non, denboraserie multzo bat eta haietako bakoitzaren klasea jakinik, helburua sailkatzailebat eraikitzea den, serie berrien klaseak iragartzeko gai dena.Denbora serieen sailkapenaren azpi-problema gisa, sailkapen goiztiarra,denboran zehar iristen den datu zerrenda bat ahalik eta lasterren klase zehatzbatean sailkatzeko nahia edo beharra dagoenean agertzen da [10]. Adibide gisa,informatika medikoan, gaixoaren datu klinikoak denboran zehar monitorizatueta jasotzen dira, eta gaixotasun batzuen detekzio goiztiarra erabakigarriada pazientearen egoeran. Esaterako, arterien buxadura, fotopletismografia(PPG) serieen bidez detektatzen da errazen [2], baina diagnosian segunduhamarren baten atzerapenak, guztiz ondorio ezberdinak ekar ditzake.Honela, tesiaren 4. kapituluan, denbora serieen datu mehatzaritzari bigarrenekarpen garrantzitsu bezala, ECDIRE (Early Classification frameworkfor time series based on class DIscriminativeness and REliability ofpredictions) izeneko denbora serieen sailkatzaile goiztiarra aurkeztu dugu.Sailkatzaile hau eraikitzeko, entrenamendu fasean, metodoak klase bakoitzaanalizatzen du eta beste klaseengandik noiztik aurrera ezberdindu daitekeenkalkulatzen du, aurrez ezarritako zehaztasun maila bat mantenduz,noski. Zehaztasun maila hau erabiltzaileak finkatuko du haren interesen arabera.Entrenamentu fase honetan lortutako informazioak sailkapenak noizegin zehaztuko digu eta, beraz, serieak goizegi esleitzea saihesten lagundukodu. Bestalde, ECDIRE metodoak sailkatzaile probabilistikoak erabiltzen ditu,eta sailkatzaile mota hauengandik lortutako a-posteriori probabilitateak,lortutako sailkapenen zehaztasuna beste era batean kontrolatzen lagundukodigu.ECDIRE metodoa UCR artxiboko 45 datu baseei aplikatu diogu, literaturanorain arte lortutako emaitzak hobetuz. Bestalde, kasu erreal bateanmetodoaren aplikazioa nolakoa izango zen erakusteko, kantuen bidezko txoriendetekzio eta identifikazio problema baterako sortutako datu base batekinere burutu ditugu esperimentuak, emaitza egokiak lortuz.Azkenik, berriro ere bidai denboren iragarpenera itzuli gara eta aurrekobi ekarpenak problema honi aplikatu dizkiogu. Lortutako emaitzetatik,problema zehatz honetarako, proposatutako bi metodoetan egin beharrekomoldaketa batzuk identifikatu ditugu. Hasteko, distantzia aukeratzeaz gain,hauen parametroak ere aukeratu behar dira. Hau egiteko silhouette bezalakoindizeak erabili ditugu, baina argitzeke dago ea metodo hau ataza honetarakoonena den. Bestalde, datuen garbiketa eta aurre-prozesatze sakon bat beharrezkoadela ere ikusi dugu, serie atipikoak eta zaratak clustering soluzioetaneragin handia baitaukate. Azkenik, gure esperimentuak iragarpen eredu historikosimpleetan oinarritu ditugu. Eredu simple hauek ordu berdinean jasotakobidai denboren batez bestekoa kalkulatuz egiten dituzte iragarpenak,eta eredu konplexuagoak erabiltzea aukera interesgarria izan daiteke.Laburbilduz, tesi honetan bidai denboren eredugintzaren literaturarenanalisi batetik hasi gara eta, bertatik abiatuta, denbora serieen mehatzaritzaribi ekarpen egin dizkiogu: lehena, denbora serie multzo bat taldekatzekodistantzia automatikoki aukeratzeko metodo baten diseinua, eta bigarrena,sailkatzaile probabilistikoetan oinarritutako denbora serieen sailkatzaile goiztiarbat. Azkenik, berriro ere bidai denboren eredugintzaren problemara itzuligara eta aurreko bi ekarpenak testuinguru honetan aplikatuko ditugu, etorkizunerakoikerketa ildo berriak zabalduz

Estimation of Travel Time using Temporal and Spatial Relationships in Sparse Data

Travel time is a basic measure upon which e.g. traveller information systems, traffic management systems, public transportation planning and other intelligent transport systems are developed. Collecting travel time information in a large and dynamic road network is essential to managing the transportation systems strategically and efficiently. This is a challenging and expensive task that requires costly travel time measurements. Estimation techniques are employed to utilise data collected for the major roads and traffic network structure to approximate travel times for minor links. Although many methodologies have been proposed, they have not yet adequately solved many challenges associated with travel time, in particular, travel time estimation for all links in a large and dynamic urban traffic network. Typically focus is placed on major roads such as motorways and main city arteries but there is an increasing need to know accurate travel times for minor urban roads. Such information is crucial for tackling air quality problems, accommodate a growing number of cars and provide accurate information for routing, e.g. self-driving vehicles. This study aims to address the aforementioned challenges by introducing a methodology able to estimate travel times in near-real-time by using historical sparse travel time data. To this end, an investigation of temporal and spatial dependencies between travel time of traffic links in the datasets is carefully conducted. Two novel methodologies are proposed, Neighbouring Link Inference method (NLIM) and Similar Model Searching method (SMS). The NLIM learns the temporal and spatial relationship between the travel time of adjacent links and uses the relation to estimate travel time of the targeted link. For this purpose, several machine learning techniques including support vector machine regression, neural network and multi-linear regression are employed. Meanwhile, SMS looks for similar NLIM models from which to utilise data in order to improve the performance of a selected NLIM model. NLIM and SMS incorporates an additional novel application for travel time outlier detection and removal. By adapting a multivariate Gaussian mixture model, an improvement in travel time estimation is achieved. Both introduced methods are evaluated on four distinct datasets and compared against benchmark techniques adopted from literature. They efficiently perform the task of travel time estimation in near-real-time of a target link using models learnt from adjacent traffic links. The training data from similar NLIM models provide more information for NLIM to learn the temporal and spatial relationship between the travel time of links to support the high variability of urban travel time and high data sparsity.Ministry of Education and Training of Vietna

Identification of infrastructure related risk factors, Deliverable 5.1 of the H2020 project SafetyCube

The present Deliverable (D5.1) describes the identification and evaluation of infrastructure related risk factors. It outlines the results of Task 5.1 of WP5 of SafetyCube, which aimed to identify and evaluate infrastructure related risk factors and related road safety problems by (i) presenting a taxonomy of infrastructure related risks, (ii) identifying “hot topics” of concern for relevant stakeholders and (iii) evaluating the relative importance for road safety outcomes (crash risk, crash frequency and severity etc.) within the scientific literature for each identified risk factor. To help achieve this, Task 5.1 has initially exploited current knowledge (e.g. existing studies) and, where possible, existing accident data (macroscopic and in-depth) in order to identify and rank risk factors related to the road infrastructure. This information will help further on in WP5 to identify countermeasures for addressing these risk factors and finally to undertake an assessment of the effects of these countermeasures. In order to develop a comprehensive taxonomy of road infrastructure-related risks, an overview of infrastructure safety across Europe was undertaken to identify the main types of road infrastructure-related risks, using key resources and publications such as the European Road Safety Observatory (ERSO), The Handbook of Road Safety Measures (Elvik et al., 2009), the iRAP toolkit and the SWOV factsheets, to name a few. The taxonomy developed contained 59 specific risk factors within 16 general risk factors, all within 10 infrastructure elements. In addition to this, stakeholder consultations in the form of a series of workshops were undertaken to prioritise risk factors (‘hot topics’) based on the feedback from the stakeholders on which risk factors they considered to be the most important or most relevant in terms of road infrastructure safety. The stakeholders who attended the workshops had a wide range of backgrounds (e.g. government, industry, research, relevant consumer organisations etc.) and a wide range of interests and knowledge. The identified ‘hot topics’ were ranked in terms of importance (i.e. which would have the greatest effect on road safety). SafetyCube analysis will put the greatest emphasis on these topics (e.g. pedestrian/cyclist safety, crossings, visibility, removing obstacles). To evaluate the scientific literature, a methodology was developed in Work Package 3 of the SafetyCube project. WP5 has applied this methodology to road infrastructure risk factors. This uniformed approach facilitated systematic searching of the scientific literature and consistent evaluation of the evidence for each risk factor. The method included a literature search strategy, a ‘coding template’ to record key data and metadata from individual studies, and guidelines for summarising the findings (Martensen et al, 2016b). The main databases used in the WP5 literature search were Scopus and TRID, with some risk factors utilising additional database searches (e.g. Google Scholar, Science Direct). Studies using crash data were considered highest priority. Where a high number of studies were found, further selection criteria were applied to ensure the best quality studies were included in the analysis (e.g. key meta-analyses, recent studies, country origin, importance). Once the most relevant studies were identified for a risk factor, each study was coded within a template developed in WP3. Information coded for each study included road system element, basic study information, road user group information, study design, measures of exposure, measures of outcomes and types of effects. The information in the coded templates will be included in the relational database developed to serve as the main source (‘back end’) of the Decision Support System (DSS) being developed for SafetyCube. Each risk factor was assigned a secondary coding partner who would carry out the control procedure and would discuss with the primary coding partner any coding issues they had found. Once all studies were coded for a risk factor, a synopsis was created, synthesising the coded studies and outlining the main findings in the form of meta-analyses (where possible) or another type of comprehensive synthesis (e.g. vote-count analysis). Each synopsis consists of three sections: a 2 page summary (including abstract, overview of effects and analysis methods); a scientific overview (short literature synthesis, overview of studies, analysis methods and analysis of the effects) and finally supporting documents (e.g. details of literature search and comparison of available studies in detail, if relevant). To enrich the background information in the synopses, in-depth accident investigation data from a number of sources across Europe (i.e. GIDAS, CARE/CADaS) was sourced. Not all risk factors could be enhanced with this data, but where it was possible, the aim was to provide further information on the type of crash scenarios typically found in collisions where specific infrastructure-related risk factors are present. If present, this data was included in the synopsis for the specific risk factor. After undertaking the literature search and coding of the studies, it was found that for some risk factors, not enough detailed studies could be found to allow a synopsis to be written. Therefore, the revised number of specific risk factors that did have a synopsis written was 37, within 7 infrastructure elements. Nevertheless, the coded studies on the remaining risk factors will be included in the database to be accessible by the interested DSS users. At the start of each synopsis, the risk factor is assigned a colour code, which indicates how important this risk factor is in terms of the amount of evidence demonstrating its impact on road safety in terms of increasing crash risk or severity. The code can either be Red (very clear increased risk), Yellow (probably risky), Grey (unclear results) or Green (probably not risky). In total, eight risk factors were given a Red code (e.g. traffic volume, traffic composition, road surface deficiencies, shoulder deficiencies, workzone length, low curve radius), twenty were given a Yellow code (e.g. secondary crashes, risks associated with road type, narrow lane or median, roadside deficiencies, type of junction, design and visibility at junctions) seven were given a Grey code (e.g. congestion, frost and snow, densely spaced junctions etc.). The specific risk factors given the red code were found to be distributed across a range of infrastructure elements, demonstrating that the greatest risk is spread across several aspects of infrastructure design and traffic control. However, four ‘hot topics’ were rated as being risky, which were ‘small work-zone length’, ‘low curve radius’, ‘absence of shoulder’ and ‘narrow shoulder’. Some limitations were identified. Firstly, because of the method used to attribute colour code, it is in theory possible for a risk factor with a Yellow colour code to have a greater overall magnitude of impact on road safety than a risk factor coded Red. This would occur if studies reported a large impact of a risk factor but without sufficient consistency to allocate a red colour code. Road safety benefits should be expected from implementing measures to mitigate Yellow as well as Red coded infrastructure risks. Secondly, findings may have been limited by both the implemented literature search strategy and the quality of the studies identified, but this was to ensure the studies included were of sufficiently high quality to inform understanding of the risk factor. Finally, due to difficulties of finding relevant studies, it was not possible to evaluate the effects on road safety of all topics listed in the taxonomy. The next task of WP5 is to begin identifying measures that will counter the identified risk factors. Priority will be placed on investigating measures aimed to mitigate the risk factors identified as Red. The priority of risk factors in the Yellow category will depend on why they were assigned to this category and whether or not they are a hot topic

Cyber physical complex networks, modeling, analysis, and control

This research scrutinize various attributes of complex networks; mainly, modeling, sensing, estimation, safety analysis, and control. In this study, formal languages and finite automata are used for modeling incident management processes. Safety properties are checked in order to verify the system. This method introduces a systematic approach to incident management protocols that are governed by mostly unsystematic algorithms. A portion of the used data in this study is collected by means of radar and loop detectors. A weighted t-statistics methodology is developed in order to validate these detectors. The detector data is then used to extract travel time information where travel time reliability is investigated. Classical reliability measures are examined and compared with the new entropy based reliability measure proposed in this study. The novel entropy based reliability measure introduces a more consistent measure with the classical definition of travel time reliability than traditional measures. Furthermore, it measures uncertainty directly using the full distribution of the examined random variable where previously developed reliability measures only use first and second moments. Various approaches of measuring network reliability are also investigated in this study. Finally, feedback linearization control scheme is developed for a ramp meter that is modeled using Godunov\u27s conditions at the boundaries representing a switched system. This study demonstrates the advantages of implementing a feedback liberalized control scheme with recursive real time parameter estimation over the commonly practiced velocity based thresholds

Ανάπτυξη προτύπου προσομοίωσης για την πρόβλεψη και τη διαχείριση έκτακτων συμβάντων σε δίκτυα αυτοκινητόδρομων

177 σ.Research on road safety has been of great interest to engineers and planners for decades. Regardless of modeling techniques, a serious factor of inaccuracy - in most past studies - has been data aggregation. Nowadays, most freeways are equipped with continuous surveillance systems making disaggregate traffic data readily available; these have been used in few studies. In this context, the main objective of this dissertation is to capitalize highway traffic data collected on a real-time basis at the moment of accident occurrence in order to expand previous road safety work and to highlight potential further applications. To this end, we first examine the effects of various traffic parameters on type of road crash as well as on the injury level sustained by vehicle occupants involved in accidents, while controlling for environmental and geometric factors. Probit models are specified on 4-years of data from the A4-A86 highway section in the Ile-de-France region, France. Empirical findings indicate that crash type can almost exclusively be defined by the prevailing traffic conditions shortly before its occurrence. Increased traffic volume is found to have a consistently positive effect on severity, while speed has a differential effect on severity depending on flow conditions. We then establish a conceptual framework for incident management applications using real-time traffic data on urban freeways. We use dissertation previous findings to explore potential implications towards incident propensity detection and enhanced management.Η Οδική Ασφάλεια αποτελεί πεδίο ερευνητικού ενδιαφέροντος για μηχανικούς κατά τις τελευταίες δεκαετίες. Ανεξάρτητα από τις εφαρμοζόμενες μεθόδους προτυποποίησης, σημαντικός παράγοντας ανακρίβειας πρότερων διερευνήσεων είναι η ομαδοποίηση δεδομένων. Ωστόσο, οι περισσότεροι αυτοκινητόδρομοι είναι πλέον εξοπλισμένοι με συστήματα παρακολούθησης, τα οποία καθιστούν διαθέσιμα μη ομαδοποιημένα κυκλοφοριακά δεδομένα. Η διαθεσιμότητα των δεδομένων αυτών δεν έχει επαρκώς αξιοποιηθεί ερευνητικά. Στόχος της διατριβής είναι η αξιοποίηση των κυκλοφοριακών δεδομένων αυτοκινητόδρομων που συλλέγονται σε πραγματικό χρόνο κατά τη στιγμή εκδήλωσης ατυχήματος. Για το σκοπό αυτό, μελετήθηκε η επίδραση διάφορων κυκλοφοριακών παραμέτρων στον τύπο οδικού ατυχήματος, αλλά και στο επίπεδο σοβαρότητας τραυματισμού των επιβαινόντων. Παράλληλα, ελήφθησαν υπόψιν παράγοντες σχετιζόμενοι με το περιβάλλον και τη γεωμετρία. Εφαρμόστηκαν μοντέλα probit σε τετραετή δεδομένα συμβάντων από το κοινό τμήμα των αυτοκινητόδρομων Α4-Α86 στην περιοχή Ile-de-France της Γαλλίας. Τα εμπειρικά αποτελέσματα καταδεικνύουν ότι ο τύπος ατυχήματος μπορεί –σχεδόν αποκλειστικά- να εκτιμηθεί από τις επικρατούσες κυκλοφοριακές συνθήκες. η αύξηση του κυκλοφοριακού φόρτου φαίνεται να ασκεί σταθερή επίδραση στη σοβαρότητα των ατυχημάτων, ενώ η επίδραση της ταχύτητας διαφοροποιείται ανάλογα με το επίπεδο του κυκλοφοριακού φόρτου. Στη συνέχεια, αναπτύσσεται πλαίσιο για την ένταξη κυκλοφοριακών δεδομένων πραγματικού χρόνου στη διαχείριση συμβάντων. Τέλος, τα πορίσματα της διατριβής χρησιμοποιούνται στη διερεύνηση εφαρμογών με απώτερο στόχο τον περιορισμό της προδιάθεσης πρόκλησης συμβάντων και τη βελτιωμένη διαχείρισή τους.Ζωή Δ. Χριστοφόρο