Development of a weight-based topological map-matching algorithm and an integrity method for location-based ITS services

The main objective of this research is to enhance navigation modules of location-based Intelligent Transport Systems (ITS) by developing a weight-based topological map-matching algorithm and a map-aided integrity monitoring process. Map-matching (MM) algorithms integrate positioning data from positioning sensors with spatial road network data to identify firstly, the road link on which a vehicle is travelling from a set of candidate links; and secondly, to determine the vehicle s location on that segment. A weight-based topological MM algorithm assigns weights for all candidate links based on different criteria such as the similarity in vehicle movement direction and link direction and the nearness of the positioning point to a link. The candidate link with the highest total weighting score is selected as the correct link. This type of map-matching algorithm is very popular due to its simplicity and speediness in identifying the correct links. Existing topological map-matching algorithms however have a number of limitations: (1) employing a number of thresholds that may not be transferable, (2) assigning arbitrary weighting coefficients to different weights, (3) not distinguishing among different operational environments (i.e., urban, suburban and rural) when determining the relative importance of different weights and (4) not taking into account all available data that could enhance the performance of a topological MM algorithm. In this research a novel weight-based topological map-matching algorithm is developed by addressing all the above limitations. The unique features of this algorithm are: introducing two new weights on turn restrictions and connectivity at junctions to improve the performance of map-matching; developing a more robust and reliable procedure for the initial map-matching process; performing two consistency checks to minimise mismatches and determining the relative importance of different weights for specific operational environments using an optimisation technique. Any error associated with either the raw positioning data (from positioning sensors) or spatial road network, or the MM process can lead to incorrect road link identification and inaccurate vehicle location estimation. Users should be notified when the navigation system performance is not reliable. This is referred to as an integrity monitoring process. In this thesis, a user-level map-aided integrity method that takes into account all error sources associated with the three components of a navigation system is developed. Again, the complexity of the road network is also considered. Errors associated with a spatial road map are given special attention. Two knowledge-based fuzzy inference systems are employed to measure the integrity scale, which provides the level of confidence in map-matching results. Performance of the new MM algorithm and the integrity method was examined using a real-world field data. The results suggest that both the algorithm and the integrity method have the potential to support a wide range of real-time location-based ITS services. The MM algorithm and integrity method developed in this research are simple, fast, efficient and easy to implement. In addition, the accuracy offered by the enhanced MM algorithm is found to be high; it is able to identify the correct links 97.8% of the time with an horizontal accuracy of 9.1 m. This implies that the developed algorithm has high potential to be implemented by industry for the purpose of supporting the navigation modules of location-based intelligent transport systems.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Development of GIS- and GPS-Based Intelligent Network-Level Road Maintenance and Rehabilitation System

A user-friendly road maintenance and rehabilitation (M&R) system has been developed to find cost effective strategies for maintaining road networks in a serviceable condition. Pavement condition data and spatial road network data were collected using a GPS palmtop, segregated and arranged spatially on a GIS platform. The M&R toolbox, developed in GIS software TransCAD macros (computer program), performs various modules which provide prioritization of maintenance of each link in the network using a priority index approach, suitable rehabilitation strategies, link-wise budget requirements, effect of available budget on vehicle operating cost and road roughness. Furthermore, additional lane requirements based on volume and capacity ratio and its design were also considered. The developed M&R system was implemented for a small part of road network in Mumbai (Bombay) metropolitan area in India. It was identified that the enhanced M&R system, developed in this study, is effective in day-to-day road maintenance and helpful in the decision making process for planning and scheduling of road M&R work

Modeling commuters’ preference towards sharing paratransit services

The transportation sector in India faces significant issues, such as congestion and air pollution, and is in dire need of sustainable strategies. Sharing vehicles is one of the popular sustainable strategies. Sharing auto-rickshaws, a paratransit mode, currently informally operating with a significant mode share, offers an opportunity to tackle sustainability issues. There are several challenges to integrating and promoting auto-rickshaw system as shared transportation using a formal structure of policies. The primary reason is a dearth of studies on sharing auto-rickshaws, leading to policymakers lacking knowledge and focus. The present study contributes to the literature to divert focus on sharing auto-rickshaws in India, considering Mumbai Metropolitan Region (MMR) as a study area. This study attempts to assess and model the intentions of users and non-users toward auto-rickshaw sharing using stated preference (SP) choice experiments and estimate Willingness-to-Pay (WTP) considering multiple socio-economic heterogeneities. Results highlight that the most critical attributes are travel time reliability and access time among different groups. Importance of having real-time information on trips among females and sharing auto-rickshaw users is high. The study provides a transparent direction toward ensuring efficient and integrated policymaking and guidelines for promoting auto-rickshaw sharing in urban areas of the Indian subcontinent with limited resources

A dynamic two-dimensional (D2D) weight-based map-matching algorithm

Existing map-Matching (MM) algorithms primarily localize positioning fixes along the centerline of a road and have largely ignored road width as an input. Consequently, vehicle lane-level localization, which is essential for stringent Intelligent Transport System (ITS) applications, seems difficult to accomplish, especially with the positioning data from low-cost GPS sensors. This paper aims to address this limitation by developing a new dynamic two-dimensional (D2D) weight-based MM algorithm incorporating dynamic weight coefficients and road width. To enable vehicle lane-level localization, a road segment is virtually expressed as a matrix of homogeneous grids with reference to a road centerline. These grids are then used to map-match positioning fixes as opposed to matching on a road centerline as carried out in traditional MM algorithms. In this developed algorithm, vehicle location identification on a road segment is based on the total weight score which is a function of four different weights: (i) proximity, (ii) kinematic, (iii) turn-intent prediction, and (iv) connectivity. Different parameters representing network complexity and positioning quality are used to assign the relative importance to different weight scores by employing an adaptive regression method. To demonstrate the transferability of the developed algorithm, it was tested by using 5,830 GPS positioning points collected in Nottingham, UK and 7,414 GPS positioning points collected in Mumbai and Pune, India. The developed algorithm, using stand-alone GPS position fixes, identifies the correct links 96.1% (for the Nottingham data) and 98.4% (for the Mumbai-Pune data) of the time. In terms of the correct lane identification, the algorithm was found to provide the accurate matching for 84% (Nottingham) and 79% (Mumbai-Pune) of the fixes obtained by stand-alone GPS. Using the same methodology adopted in this study, the accuracy of the lane identification could further be enhanced if the localization data from additional sensors (e.g. gyroscope) are utilized. ITS industry and vehicle manufacturers can implement this D2D map-matching algorithm for liability critical and in-vehicle information systems and services such as advanced driver assistant systems (ADAS)

Developing an enhanced weight-based topological map-matching algorithm for intelligent transport systems

Map-matching (MM) algorithms integrate positioning data from a Global Positioning System (or a number of other positioning sensors) with a spatial road map with the aim of identifying the road segment on which a user (or a vehicle) is travelling and the location on that segment. Amongst the family of MM algorithms consisting of geometric, topological, probabilistic and advanced, topological MM (tMM) algorithms are relatively simple, easy and quick, enabling them to be implemented in real-time. Therefore, a tMM algorithm is used in many navigation devices manufactured by industry. However, existing tMM algorithms have a number of limitations which affect their performance relative to advanced MM algorithms. This paper demonstrates that it is possible by addressing these issues to significantly improve the performance of a tMM algorithm. This paper describes the development of an enhanced weight-based tMM algorithm in which the weights are determined from real-world field data using an optimisation technique. Two new weights for turn-restriction at junctions and link connectivity are introduced to improve the performance of matching, especially at junctions. A new procedure is developed for the initial map-matching process. Two consistency checks are introduced to minimise mismatches. The enhanced map-matching algorithm was tested using field data from dense urban areas and suburban areas. The algorithm identified 96.8% and 95.93% of the links correctly for positioning data collected in urban areas of central London and Washington, DC, respectively. In case of suburban area, in the west of London, the algorithm succeeded with 96.71% correct link identification with a horizontal accuracy of 9.81 m (2σ). This is superior to most existing topological MM algorithms and has the potential to support the navigation modules of many Intelligent Transport System (ITS) services

Slum rehabilitation in the context of urban sustainability: a case study of Mumbai, India

In the last two decades, migration from villages and small towns to metropolitan areas has increased tremendously in India. This leads to the degradation of urban environmental quality and sustainable development especially in the metropolitan cities. The problems faced by the people living in the urban areas of India have become major concerns for the government over the last two decades. Slums are considered to be the major issue within many urban areas; particularly problems related to transportation, population, health and safety. India is one of the fastest developing countries with many metropolitan cities (e.g. Mumbai, Pune, Bangalore, Hyderabad, Delhi and Chennai). To explore the effect of rehabilitation of slums on urban sustainability, part of Mumbai was selected as a case study. Compared to the other metropolitan cities in India, Mumbai is one of the biggest metropolitan regions and capital of the state of Maharashtra with many slums varying in sizes. In addition, every year millions of rupees are being spent to resettle and rehabilitate slums to make Mumbai sustainable. It is reported that around 6 percent of the total land holds nearly 60 percent of the total Mumbai population (CBC, 2006). From 1980 onwards, the rate of migration and the sprawling nature of slums into the city has become an major issue, although many organisations are working towards development of Mumbai, the conditions are not conducive to achieving urban sustainable environment as most of the organisations are not working on a united front. Also, various researchers have reported that to maintain the pace of sustainable urbanisation, a holistic approach to sustainable development needs to be considered. Considering today’s poor urban environmental quality in Mumbai, there are many projects under development and execution to improve the poor conditions. Also, the World Bank has funded many projects with the primary aim of improving the city’s land transport, health and education which affect thousands of families. The majority of families affected by urban development projects are located in slum areas which are under consideration for resettlement and/ or rehabilitation. The aim of this research is to examine slum areas and their effects on sustainable urban development. To accomplish the above aim, a case study based approach, engaging a series of face‐to‐face interviews, was used. As a part of this research, an urban development project funded by the World Bank to achieve urban sustainability in Mumbai Metropolitan Region (MMR) was explored. Also, several visits to other slums and rehabilitated areas were conducted to identify the quality of life in slums and rehabilitated areas. The data collected during the face‐to‐face interviews, was used for descriptive analysis considering various aspects (i.e. social, educational) of urban sustainability. Through this research, the reasons for slums and problems related to slums were explored. During the research, it is revealed that some people still think that urbanisation is responsible for unsustainable development and they are not in favour of resettlement and rehabilitation. This suggests that to achieve successful urban sustainability, other issues such as employment, education and general awareness are also required along with low‐cost mass housing

Impacts of speed variations on freeway crashes by severity and vehicle type

Speed variations are identified as potentially important predictors of freeway crash rates; however, their impacts on crashes are not entirely known. Existing findings tend to be inconsistent possibly because of the different definitions for speed variations, different crash type consideration or different modelling and data aggregation approaches. This study explores the relationships of speed variations with crashes on a freeway section in the UK. Crashes split by vehicle type (heavy and light vehicles) and by severity mode (killed/serious injury and slight injury crashes) are aggregated based on the similarities of the conditions just before their occurrence (condition-based approach) and modelled using Multivariate Poisson lognormal regression. The models control for speed variations along with other traffic and weather variables as well as their interactions. Speed variations are expressed as two separate variables namely the standard deviations of speed within the same lane and between-lanes over a five minute interval. The results, similar for all crash types (by coefficient significance and sign), suggest that crash rates increase as the within lane speed variations raise, especially at higher traffic volumes. Higher speeds coupled with greater volume and high between-lanes speed variation also increase crash likelihood. Overall, the results suggest that specific combinations of traffic characteristics increase the likelihood of crash occurrences rather than their individual effects. Identification of these specific crash prone conditions could improve our understanding of crash risk and would support the development of more efficient safety countermeasures

Achieving genuinely dynamic road user charging : issues with a GNSS-based approach

Peer reviewedPostprin

Exploring the effectiveness of a digital voice assistant to maintain driver alertness in partially automated vehicles

Objective: Vehicle automation shifts the driver's role from active operator to passive observer at the potential cost of degrading their alertness. This study investigated the role of an in-vehicle voice-based assistant (VA; conversing about traffic/road environment) to counter the disengaging and fatiguing effects of automation.Method: Twenty-four participants undertook two drives– with and without VA in a partially automated vehicle. Participants were subsequently categorized into high and low participation groups (based on their proportion of vocal exchanges with VA). The effectiveness of VA was assessed based on driver alertness measured using Karolinska Sleepiness Scale (KSS), eye-based sleepiness indicators and glance behavior, NASA-TLX workload rating and time to gain motor readiness in response to take-over request and performance rating made by the drivers.Results: Paired samples t-tests comparison of alertness measures across the two drives were conducted. Lower KSS rating, larger pupil diameter, higher glances (rear-mirror, roadside vehicles and signals in the drive with VA) and higher feedback ratings of VA indicated the efficiency of VA in improving driver alertness during automation. However, there was no significant difference in alertness or glance behavior between the driver groups (high and low-PR), although the time to resume steering control was significantly lower in the higher engagement group.Conclusion: The study successfully demonstrated the advantages of using a voice assistant (VA) to counter these effects of passive fatigue, for example, by reducing the time to gain motor-readiness following a TOR. The findings show that despite the low engagement in spoken conversation, active listening also positively influenced driver alertness and awareness during the drive in an automated vehicle

Effects of driver work-rest patterns, lifestyle and payment incentives on long-haul truck driver sleepiness

The aim of the study is to identify and model the role of payment incentives, driver work-rest patterns and other lifestyle habits influencing the drowsy driving behavior among long-haul truck drivers. To achieve this aim, this study targeted two main objectives: (1) to examine the significant differences between the groups of drowsy and non-drowsy drivers based on the opportunities of monetary incentives and (2) to examine the role of different factors: driver demographics, work-rest patterns, lifestyle and occupational characteristics particularly incentives associated with driving in causing driver sleepiness among Indian truck drivers. The study is based on interview responses from 453 long-haul truck drivers approached in three Indian cities- Mumbai, Indore and Nagpur. Initial principal component analysis of the responses related to financial incentives (occupational characteristics) resulted into two correlated factors: (i) willingness to earn extra payments if offered (WEP) and (ii) incentives available in the current driving experience (ICD) that influence driver work-rest patterns and alertness while driving. Kruskal-Wallis test showed a significant difference among the groups of sleepy and non-sleepy drivers due to these factors (WEP and ICD). Finally, a logistic regression model showed that long driving duration, working days per week, rest patterns, insufficient sleeping hours and history of violations were found significantly associated with drowsy driving among the long-haul truck drivers. Increase in consumption of caffeine and tobacco indicated reduction in driver alertness. According to the model results, the odds of drowsy driving were 77% less for drivers between 46-55 years compared to the young drivers (<25 years). Driving under the influence of financial incentives was observed to increase the odds of falling asleep by 1.58 times among the truck drivers. This was apparently the most interesting and intriguing result of the study indicating the need for further research on the influence of financial or socio-economic motivations to sleepiness