Search

This study uses survival models to estimate the arrival time of buses at the downstream stops and intersections. Both accelerated failure time (AFT) and Cox regression based hazard models were considered in this study. Two different types of events: (i) buses arriving at bus stops and (ii) buses arriving at signalised intersections were included for measuring arrival times. Weibull and log-logistic distribution models were fitted for obtaining the arrival times against both the events separately. Various other factors such as distance, speed, bus stop dwell time, passenger count, gradient of the road, intersection length and signal details which included green time, red time, cycle length and so on were considered as explanatory variables. The proposed study was tested on a study corridor of length 59.48 km in the Mumbai arterial roads using public transport (buses). The results reveal that arrival times predicted using the developed models provided smaller uncertainties for 70% of the prediction and reduced prediction variation by 10%. The mean absolute percentage error value obtained for the AFT survival models was 10.04. Overall, the AFT model approach appears to be a promising method compared to Cox regression to predict bus arrival times and the associated uncertainties.

Vehicle automation allows drivers to disengage from driving causing a potential decline in their alertness. One of the major challenges of highly automated vehicles is to ensure a timely (with respect to safety and situation awareness) takeover in such conditions. For this purpose, the current study investigated the role of an in-vehicle digital voice-assistant (VA) in conditionally automated vehicles, offering spoken discourse relating specifically to contextual factors, such as the traffic situation and road environment. The study involved twenty-four participants, each taking two drives (counterbalanced): with VA and without VA, in a driving simulator. Participants were required to takeover vehicle control following the issuance of a takeover request (TOR) near the end of each drive. A parametric duration model was adopted to find the key factors determining takeover time (TOT). Paired comparisons showed higher alertness and higher active workload (mean NASA-TLX rating) during automation when accompanied by the VA. Paired t-test comparison of gaze behavior prior to takeover showed significantly higher instances of checking traffic signal, roadside objects, and the roadway during the drive with VA, indicating higher situation awareness. The parametric model indicated that the VA increased the likelihood of making a timely takeover by 39%. There was also some evidence suggesting that male drivers are likely to resume control 1.21 times earlier than female drivers. The study findings highlight the benefits of adopting a digital voice assistant to keep the drivers alert and aware about the recent traffic environment in partially automated vehicles.

Driving under the influence of alcohol impairs the driving performance and increases the crash risks worldwide. The present study investigated the effect of different Blood Alcohol Concentrations (BAC) on the reaction time of Indian drivers of two age groups (Young: less than 25 years; Mature: 25 years and above) through a driving simulator experiment. Reaction times were measured for the two hazardous events: (1) pedestrian crossing event and (2) road crossing event by parked vehicles. Seventy nine licensed drivers drove on the driving simulator under the influence of four BAC levels (0.00%, 0.03%, 0.05% and 0.08%). To analyse the reaction times, two Weibull AFT (Accelerated Failure Time) models with gamma frailty were developed for both the events separately by incorporating the different alcohol conditions and driver characteristics (such as driver’s age, driving experience and frequent consumption of alcohol) as the explanatory variables. It was observed that the BAC levels of 0.03%, 0.05% and 0.08% resulted in 36%, 53% and 94% increment in the reaction times of the drivers for the pedestrian crossing event and 64%, 78% and 116% increment in the reaction times for road crossing event by the parked vehicles respectively. A year increase in the driving experience resulted in 2% reduction in the reaction times, and mature drivers responded 15% faster than the young drivers. Interestingly, frequent drinkers showed 11% quicker response than the drivers who drink rarely. Overall, the results showed that the stimulus-response behaviour of the drivers was impaired in all the intoxicated states. The research findings may assist the countermeasure studies and policy-making against alcohol-impaired driving.

The objective of this study is to develop an accurate model for corridor-level travel-time estimation. Different approaches, such as k-nearest neighbour (k-NN), gradient boosting decision tree (GBDT) and support vector machines (SVMs), were used in this study. Further, this study also developed a hybrid model combining a data-driven approach (SVM) and a model-based approach [particle filter (PF)] for corridor-level travel-time estimation. Both static and dynamic parameters, such as road geometry, intersection length, location information from Global Positioning System devices, dwell time etc. were used as influential factors for modelling. The proposed algorithm was tested on a study corridor of length 59.48 km, in the arterials of Mumbai, India. The data was collected using a probe-vehicle technique for five days during the morning peak period (from 8.00 am to 11.00 am) for two modes (car and bus). The mean absolute percentage error values obtained for the hybrid model for the two modes were: 9.96 (car) and 11.24 (bus). The performance of the proposed hybrid (SVM-PF) algorithm showed a clear improvement in accuracy in comparison to existing standard methods such as k-NN, GBDT and SVM.

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.

Safety assessment among sleep-deprived drivers is a challenging research area with only a few sleep-related studies investigating safety performance during car-following. Therefore, this study aimed to measure the effects of partial sleep deprivation on driver safety during car-following. Fifty healthy male drivers with no prior history of any sleep-related disorders, drove the driving simulator in three conditions of varying sleep duration: a baseline (no sleep deprivation), test session (TS1) after one night of PSD (sleep ≤4.5 h/night) and TS2 after two consecutive nights of PSD. The reduced sleep in PSD sessions was monitored using an Actiwatch. Karolinska Sleepiness Scale was used to indicate loss of alertness among drivers. Each drive included a car-following task to measure longitudinal safety indicators based on speed and headway management: normalized time exposed to critical gap (TECG’), safety critical time headway and speed variability with respect to leading vehicle’s speed (SPV). Crash potential index (CPI) was also determined from deceleration rate of drivers during car-following and was found correlated with other indicators. Therefore, to determine the aggregate influence of PSD on safety during car-following, CPI was modelled in terms of TECG, SPV, THW and other covariates. All safety metrics were modelled using generalized mixed effects regression models. The results showed that compared to the baseline drive, critical time headway decreased by 0.65 and 1.08 times whereas speed variability increased by 1.34 and 1.28 times during the TS1 and TS2, respectively, both indicating higher crash risk. However, decrease in TECG’ by 64 % and 56 % during TS1 and TS2, respectively indicate compensatory measures to avoid risks due to sleep loss. A fractional regression model of crash potential revealed that low time-headway and higher speed variability and high time exposed to critical gap (TECG’) significantly contribute to higher CPI values indicating higher safety risk. Other covariates such as sleep duration, professional driving experience and history of traffic violations were also associated with safety indicators and CPI, however no significant effects of age were noticed in the study. The study findings present the safety indicators sensitive to rear-end crashes specifically under PSD conditions, which can be used in designing collisions avoidance systems and strategies to improve overall traffic safety.

Driving error is an intrinsic component of unsafe driving behaviour. Even though alcohol results in substantial crash fatalities on Indian roads, there is hardly any evidence-based research on alcohol impairment in the driving errors of Indian drivers. The present study examined the effects of differentiated Blood Alcohol Concentration (BAC) levels (0%, 0.03%, 0.05%, and 0.08%) on the driving errors of Indian drivers. 82 volunteers participated in the driving simulator study and drove in rural and urban environments in a randomized and counterbalanced order. Generalized negative binomial regression models were developed for longitudinal and lateral driving error measures. Compared to the sober state, the increments in speed limit violations were 53%, 138% and 143% in rural driving environment, and 68%, 89%, 95% in urban driving environment at 0.03%, 0.05% and 0.08% BACs. While 0.03% and 0.05% BACs did not significantly influence the lane excursions in rural environment, 0.08% BAC resulted in 107% increment; corresponding increments were 71%, 84%, and 148% in urban environment. In rural environment, 0.03%, 0.05% and 0.08% BACs resulted in 28%, 39% and 58% increments in low RPM frequencies (RPM 5000). In urban environment, the increments were 35%, 56% and 71% for low RPMs at 0.03%, 0.05% and 0.08% BACs; whereas, for high RPMs, the increments at 0.05% and 0.08% BACs were 45% and 67% respectively (no significant influence of 0.03% BAC). The study identified the significant risk factors influencing driving errors, thereby sketching the risky driving profiles.

Real-time passenger information (RTPI) systems have been identified as having benefits in terms of passenger willingness to travel by public transport and their satisfaction levels with services provided. The lack of this amenity in rural areas, however, may hamper public transport use, thus reinforcing patterns of over-reliance on personal vehicles. To explore the potential impacts of providing RTPI in rural areas, a smartphone application ( GetThereBus ) was developed to allow rural bus passengers to share real-time public transport data, and access real-time and timetable information. Through user testing of GetThereBus , this work aimed to address questions related to the impact of the limited availability of rural digital infrastructure on the provision of RTPI; the potential for crowdsourced information to supplement published timetable information given digital limitations; and the potential impacts of such a system on the traveller experience. This study describes the GetThereBus development and evaluation phases. The authors found it was possible to design and develop a system that overcame many of the technological limitations experienced in rural areas, and users reported a positive response to the system. However, despite a campaign of user engagement, it proved difficult to recruit and motivate sufficient users to provide the data needed to achieve area-wide coverage.

Distracted driving due to mobile phone use has been identified as a major contributor to accidents; therefore, it is required to develop ways for detecting driver distraction due to phone use. Though prior literature has documented various visual behavioural and physiological techniques to identify driver distraction, comparatively little is known about vehicle based performance features which can identify driver’s distracted state during phone conversation and texting while driving. Therefore, this study examined the effects of simple conversation, complex conversation, simple texting and complex texting tasks on vehicle based performance parameters such as standard deviation of lane positioning, number of lane excursions, mean and standard deviation of lateral acceleration, mean and standard deviation of steering wheel angle and steering reversal rates (for 1°, 5° and 10° angle differences). All these performance measures were collected for 100 licensed drivers, belonging to three age groups (young, mid-age and old age), with the help of a driving simulator. Effects of all the phone use conditions and driver demographics (age, gender and phone use habits) on the measures were analysed by repeated measures ANOVA tests. Results showed that 1°, 5° SRRs are able to identify all the distracted conditions except for simple conversation; while, 10° SSR can detect all the distracted conditions (including simple conversation). The results suggest that 10° SRR can be included in intelligent in-vehicle devices in order to detect distraction and alert drivers of their distracted state. This can prevent mobile phone use during driving and therefore can help in reducing the road accidents due to mobile phone distractions.

Male and female drivers exhibit different degrees of vehicle control while driving under the influence of alcohol. However, this interaction between alcohol and gender is understudied. The present study examined the effects of different alcohol levels on the driving control of male and female drivers with the help of driving simulator experiments in heterogeneous traffic conditions.Forty young drivers (20 males and 20 females) completed simulated driving at four Blood Alcohol Concentration (BAC) levels: 0% (control), 0.03%, 0.05% and 0.08%. Driving impairment in vehicle control was measured in terms of average speed, acceleration variability and reaction time of drivers. Repeated-measures ANOVA tests were conducted and regression models were developed for male and female drivers to quantify the effects of BAC levels and driver characteristics on the driving control measures.Significant effects of gender were observed for average speed (p 0.001) and acceleration variability (p = 0.015) but not for reaction time of drivers (p = 0.891). Further, the effect of BAC was significant in all the three measures of vehicle control (p 0.001). Driving control improved with increasing age of male drivers while caffeine consumption was observed as an alcohol-antagonizing factor in female drivers.The findings suggest that vehicle control of female drivers is more likely to get affected even at low BAC levels, providing evidence that they belong to critical section of driving community in terms of alcohol-related impairment. The findings may help in discouraging drinking and driving among male and female drivers.

Background Alcohol-impaired driving is a critical factor leading to traffic crashes worldwide. Since decades, researchers from the developed nations have been investigating the effects of alcohol intoxication on their drivers. However, alcohol effects on the drivers of developing counties remain understudied. The extent of alcohol impairment may vary among the drivers of developed and developing nations due to the differences in drivers’ physiology, driving conditions, drinking habits, policy enforcement and driving attitude. Methods This study examined the effects of different Blood Alcohol Concentration (BAC) levels (0%, 0.03%, 0.05%, and 0.08%) on 75 licensed Indian drivers (56 males and 19 females) with the help of a fixed-base driving simulator. Subjects completed driving simulations for all the BAC levels in a driving environment representing heterogeneous traffic conditions. Performance measures included longitudinal and lateral driving control indicators (obtained from simulator) along with the self-reported physiological changes in the drivers (obtained from questionnaire) with varying BAC levels. Results Mean speed was the only performance measure significantly affected at 0.03% BAC. At 0.05% BAC, mean speed and mean steering wheel angle were the two significantly impaired measures. At 0.08% BAC, all the driving performance measures showed significant impairment except steering wheel angle variability. Physiological characteristics of drivers deteriorated with rising BAC levels. Conclusions Alcohol significantly impaired the physiology of Indian drivers resulting in impairment in their driving performance. Mean speed was the only performance measure significantly affected by all the BAC levels, making it a suitable parameter to detect the alcohol-impaired state of the drivers.

Globalisation, elimination of trade barriers, lower tariffs, and industrial revolutions along with technological advancement, have changed the way for freight transportation. Trade-related internat...

There is a lack of studies on assessing long-term impacts of different sectors on shrinking global carbon budget. Present study focuses on assessing contribution of electric vehicles (EVs) with improving electricity grids’ efficiency (EGs) and minimizing ‘transmission and distribution’ (T&D) losses in reducing carbon dioxide (CO2) emissions and fulfilling emission targets for 2030 and 2050. Eleven different scenarios are articulated with estimated uncertainties for Mumbai Metropolitan Regions’ (MMRs’) passenger transportation using data extracted from MMR’s Comprehensive Mobility Plan’s transportation model. A novel methodology is developed to estimate local carbon budget targets from global targets between 2018 and 2050. The estimations indicate that carbon emissions in ‘business as usual’ scenario are 252%, 505%, and 55% above the targets for 2 °C, 1.5 °C, and Intended Nationally Determined Contributions (INDC), respectively. Study also shows that introducing EVs in MMR without simultaneously improving EGs will generate more net CO2 emissions than BAU. EVs with EG and T&D improvements are not enough to meet the estimated targets. However, their combined contribution significantly reduces CO2 emission by half and reaches peak emission by 2025 from post-half-century.

Individuals in rural areas are often provided with little or no information regarding public transport disruptions. This has strong impact on those with limited access to alternative motorised transport. Though an increasing number of real time passenger information (RTPI) systems are being developed their role in supporting travellers during service disruption is poorly understood, particularly in rural areas. In this paper, we first illustrate and categorise travel disruptions. Requirements for RTPI — particularly for rural public transport users — are then identified for each type and stage of disruption through interviews and focus groups with rural passengers. Patterns of passenger behaviour during travel and transport disruptions are also classified. Further, a conceptual model of the recovery phases of disruption has been developed to map the RTPI requirements for each recovery phase of disruption. The model has been developed and evaluated through a series of focus groups and interviews with passengers, transport service providers, and government agencies. Finally, the paper also discusses and suggests the necessary advances in digital technologies for RTPI systems required to support public transport users during disruptions and thus minimise the number of trips abandoned.

The outbreak of COVID-19 pandemic has resulted in change in both commute and personal travel patterns. Though, in India, lockdown was implemented from 25th March 2020, due to self-awareness and pandemic risk perception, change in commuter behavior was observed from the beginning of March 2020. The period from 15th to 24th March 2020 is considered as the transition phase of COVID-19 outbreak in India (i.e., between no lockdown and lockdown period). This study attempts to use a decision tree approach to investigate the modal preference of 1542 commuters in association with socio-economic and travel characteristics, and safety perceptions with respect to public and private modes during transition to lockdown due to COVID-19 in India. About 41% of commuters stopped travelling during the transition to lockdown phase, 51.3% were using the same mode of transport and 5.3% of commuters shifted from public to private mode. The study findings reported different interactions of factors influencing the decision to use public or private modes of transport for daily commuting during pandemic situations like COVID-19. Interestingly, safety perceptions (associated with personal health) of commuters did not play a significant role in their mode choice behavior during the transition phase. Though people perceived public transportation as unsafe over personal vehicle use, the actual commute patterns did not validate this due to a possible reason that commuters do not have enough alternative modes. Given the uncertainties in the decision making of the commuters regarding their travel behavior due to physical distancing, the insights from this study are important to policymakers and local transport authorities to understand the change in travel patterns., Highlights • India went into complete lockdown from 25th March 2020 due to COVID-19 outbreak. • The period between 15th to 24th March 2020 is considered as the transition phase (between no lockdown and lockdown). • Mode choice preferences of 1542 commuters were examined with respect to public and private modes during transition phase in India. • Safety perception of commuters did not play a significant role in mode choice behaviour during the transition phase. • Study insights are important to the policymakers and transport authorities to understand the change in travel patterns.

This study aimed at modeling the Response Time (RT) and Total Braking Time (TBT) of drivers under Partial Sleep Deprivation (PSD). Fifty male participants drove the driving simulator in three experimental conditions: two test sessions and a baseline. The two test sessions were conducted after one and two nights of PSD (sleep = 4.25 ± 0.5 h), respectively. Sleep reduction was recorded using a wrist-worn Actiwatch. The baseline session was conducted after full rest (7-8 h sleep/day for a week). The order of test sessions and baseline was randomized. Each test included two hazard events: 1) pedestrians crossing a road and 2) parked vehicles merging into a roadway. Karolinska Sleepiness Scale (KSS) and Sleepiness Symptoms Questionnaire (SSQ) ratings were also recorded during each drive. Four separate models using parametric accelerated failure time (AFT) with Weibull distribution were developed for RT and TBT in the two events. The models were chosen with clustered heterogeneity to account for intra-group heterogeneity due to repeated measures across tests. In the case of pedestrians crossing event, RT increased by 10% in the first test session and no significant effect observed on RT in the second test session. The overall TBT reduced by 25% and 28% during the first and second PSD sessions, respectively. In the case of vehicle merging event, both response time and total braking time delayed by 44% and 17% respectively after PSD. Other factors such as age, experience, work-rest hours, KSS and SSQ rating, often exercising, approaching speed and braking force were also found significant in the analysis. The parametric AFT approach adopted in this study showed the change in ‘response time’ and ‘total braking time’ concerning the type of hazard scenario and partial sleep-deprivation.

The availability of real-time passenger information (RTPI) is a key factor in making public transport both accessible and attractive to users. Unfortunately, rural areas often lack the infrastructure necessary to provide such information, and the cost of deploying and maintaining the required technologies outside of urban areas is seen as prohibitive. In this paper we present the GetThere system developed to overcome such issues and to provide public transport users in rural areas with RTPI. An ontological framework for representing mobility information is described, along with the Linked Data approach used to integrate heterogeneous data from multiple sources including government, transport operators, and the public. To mitigate possible issues with the veracity of this data, a quality assessment framework was developed that utilises data provenance. We also discuss our experiences working with Semantic Web technologies in this domain, and present results from both a user trial and a performance evaluation of the system. (C) 2017 Elsevier B.V. All rights reserved.

In this research, a real-time positioning method, which utilises crowdsourced positioning data obtained from smartphone GPS is developed. Such vehicle location information obtained from crowdsourcing and smartphones in public transport could replace traditional automatic vehicle location systems. However, the location information from smartphone GPS is more erroneous. The proposed methodology serves as an alternative to existing positioning methods to improve the vehicle positioning accuracy. The developed enhanced particle filter algorithm takes smartphone GPS positioning data [from multiple passengers in a single transit vehicle (e.g. bus)] as input data. This `crowdsourced' data can then be utilised to calculate the vehicles' positioning information with better accuracy using the developed enhanced particle filter algorithm. The developed algorithm was tested using data collected on 14 different bus routes in urban and suburban areas of Mumbai, India, and it was identified that the algorithm is effective in reducing the average error up to 21.3% from a regular smartphone GPS and 10% from extended Kalman filter algorithm and was able to curtail positioning error within 8.672 m (average over 14 routes).

Phone use during driving causes decrease in situation awareness and delays response to the events happening in driving environment which may lead to accidents. Reaction time is one of the most suitable parameters to measure the effect of distraction on event detection performance. Therefore, this paper reports the results of a simulator study which analysed and modelled the effects of mobile phone distraction upon reaction time of the Indian drivers belonging to three different age groups. Two different types of hazardous events: (1) pedestrian crossing event and (2) road, crossing event by parked vehicles were included for measuring drivers' reaction times. Four types of mobile phone distraction tasks: simple conversation, complex conversation, simple texting and complex texting were included in the experiment. Two Weibull AFT (Accelerated Failure Time) models were developed for the reaction times against both the events separately, by taking all the phone use conditions and various other factors (such as age, gender, and phone use habits during driving) as explanatory variables. The developed models showed that in case of pedestrian crossing event, the phone use tasks: simple conversation, complex conversation, simple texting and complex texting caused 40%, 95%, 137% and 204% increment in the reaction times and in case of road crossing event by parked vehicles, the tasks caused 48%, 65%, 121% and 171% increment in reaction times respectively. Thus all the phone use conditions proved to be the most significant factors in degrading the driving performance. (C) 2017 Elsevier Ltd. All rights reserved.

Flexible transport systems (FTS) can offer an alternative to fixed route fixed schedule bus services in situations of low and dispersed demand. Although it is widely recognised that these on-demand services provide a better level of service to passengers at a lower operating cost than the infrequent fixed route services which they often replace, they still require significant financial support to cover their costs. As local authority budgets to support public transport continue to be reduced the sustainability of these services is being brought into sharper focus. In this paper we compare and contrast the development of FTS in UK, Japan and India. In particular we examine the extent to which new technologies are being used to contribute to a reduction in operating costs in the UK and Japan and whether these developments point to a future model for sustainable flexible service provision in developing countries, using India as an example.

In recent decades, use of demand-responsive and flexible transport services (e.g. dial-a-ride services, shared taxicabs, car-club) in rural areas has been promoted as a promising solution to connect rural dwellers to appropriate transport nodes (such as train stations/bus stops). It is accepted that such services, that aim to provide the desired level of flexibility to passengers, have the potential to improve accessibility and connectivity in remote and rural areas. Building on an on-going programme of Digital Economy research, the main aim of this paper is to present a passengercentric information eco-system for bus passengers and evaluate its applicability for rural flexible transport systems. To achieve the above aim, the following objectives are formulated: (1) to understand the various operational and technology-related deployment issues with FTS in rural areas; (2) to describe the development of a passenger-centric information system; and (3) to establish the anticipated usefulness of the developed information eco-system for various FTS services.

This study analysed and modelled the effects of conversation and texting (each with two difficulty levels) on driving performance of Indian drivers in terms of their mean speed and accident avoiding abilities; and further explored the relationship between speed reduction strategy of the drivers and their corresponding accident frequency. 100 drivers of three different age groups (young, mid-age and old-age) participated in the simulator study. Two sudden events of Indian context: unexpected crossing of pedestrians and joining of parked vehicles from road side, were simulated for estimating the accident probabilities. Generalized linear mixed models approach was used for developing linear regression models for mean speed and binary logistic regression models for accident probability. The results of the models showed that the drivers significantly compensated the increased workload by reducing their mean speed by 2.62 m/s and 5.29 m/s in the presence of conversation and texting tasks respectively. The logistic models for accident probabilities showed that the accident probabilities increased by 3 and 4 times respectively when the drivers were conversing or texting on a phone during driving. Further, the relationship between the speed reduction patterns and their corresponding accident frequencies showed that all the drivers compensated differently; but, among all the drivers, only few drivers, who compensated by reducing the speed by 30% or more, were able to fully offset the increased accident risk associated with the phone use.

Objective: Road crashes are considered as the eighth leading causes of death. There is a wide disparity in crash severity and law enforcement efficiency among low-, medium-, and high-income countries. It would be helpful to review the crash severity trends in these countries, identify the vulnerable road users, and understand the law enforcement effectiveness in devising efficient road safety improvement strategies.Method: The crash severity, fatality rate among various age groups, and law enforcement strategies of 10 countries representing low-income (i.e., India and Morocco), medium-income (i.e. Argentina, South Korea, and Greece), and high-income (i.e., Australia, Canada, France, the UK, and the United States) are studied and compared for a period of 5years (i.e., 2008 to 2012). The critical parameters affecting road safety are identified and correlated with education, culture, and basic compliance with traffic safety laws. In the process, possible road safety improvement strategies are identified for low-income countries.Results: The number of registered vehicles shows an increasing trend for low-income countries as do the crash rate and crash severity. Compliance related to seat belt and helmet laws is high in high-income countries. In addition, recent seat belt- and helmet-related safety programs in middle-income countries helped to curb fatalities. Noncompliance with safety laws in low-income countries is attributed to education, culture, and inefficient law enforcement.Conclusion: Efficient law enforcement and effective safety education taking into account cultural diversity are the key aspects to reduce traffic-related injuries and fatalities in low-income countries like India.

Throughout the world application of vehicle and infrastructure based intelligent transportation systems have been increased over the last decade. Most of these advanced intelligent technologies are only used in urban areas. Successful introduction of such intelligent technologies can enhance the level of safety, security and efficiency of transportation system in rural communities and support country's growth. In this research, a techno-economic feasibility evaluation of a smart street light (SSL) system, in the context of rural areas in India, has been carried out. In order to evaluate the proposed SSL system we have considered a case study of the remotely located Khurhaan village in the state of Bihar, India. The result suggested that adopting SSL over the conventional street light system gives a cost saving of 91,506 annually.

Flexible transport systems (FTS) offer a promising approach to improving the efficiency and performance of passenger transportation services. FTS aim to provide passengers with flexibility in choosing routes, times, modes of transport, service provider and payment systems. In order to achieve this additional flexibility, a well-designed FTS integrate different modes of transport, possibly spanning multiple service providers, to provide more sophisticated, comfortable and cost-effective transport opportunities. The concept of flexible transport is not new; many existing systems, including shared taxicabs, Dial-A-Ride services, and car-clubs, contain elements of such a system. In this paper, we concentrate on FTS within rural areas, which generally suffer from lack of service availability and demand uncertainties, and for which existing FTS solutions are not well suited. We present an agent-based flexible transport systems platform developed using argumentation theory. Formal argumentation is a powerful technique borrowed from artificial intelligence, and in this context is used to weigh-up the conflicting choices available to both passengers and service providers. The resultant platform for FTS in rural areas acts as a virtual transport market place that would more effectively match existing demand and supply for transport services than existing solutions.

The concept of user-level integrity monitoring has been successfully applied to air transport navigation systems, where the main focus is on the errors associated with the Global Positioning System (GPS)-data-processing chain. Little research effort has been devoted to the study of integrity monitoring for the case of land vehicle navigation systems. The primary difference is that it is also necessary to consider errors associated with a spatial map and a map-matching (MM) process when monitoring the integrity of a land vehicle navigation system. This is because these two components play a vital role in land vehicle navigation. To date, research has focused on either the integrity of raw positioning data obtained from GPS or the integrity of the MM process and digital map errors. In this paper, these sources of error are simultaneously considered. Therefore, the main contribution of this paper is to report the development of a user-level integrity-monitoring system that concurrently takes into account all the potential error sources associated with a navigation system and considers the operational environment to further improve performance. Errors associated with a spatial road map are given special attention. Two knowledge-based fuzzy inference systems were developed to measure the integrity scale. The performance of the integrity method was assessed using field data collected in Nottingham and London, U.K. The results indicate that the integrity method provides valid warnings 98.2% and 99.4% of the time for positioning data in a mixed operational environment in Nottingham and suburban areas of London, respectively.

Map-matching algorithms integrate data from positioning sensors with a digital map in order, first, to identify the road link on which a vehicle is traveling, and second, to determine the vehicle's location on that link. Due to errors in positioning sensors, digital maps, and the map-matching (MM) process, MM algorithms sometimes fail to identify the correct road segment from the candidate segments. This phenomenon is known as mismatching. Identification of the wrong road link may mislead users and degrade the performance of a location-based intelligent transportation system (ITS) and services. The main objective of this article is to improve a topological map-matching (tMM) algorithm by error detection, correction, and performance re-evaluation. Errors in a tMM algorithm were determined using data comprising 62,887 positioning points collected in three different countries (the United Kingdom, the United States, and India). After map-matching, each mismatched case was examined to identify the primary causes of the mismatches. A number of strategies were developed and applied to reduce the risk of mismatching thus enhancing the tMM algorithm. An independent data set of 5,256 positioning points collected in and around Nottingham, UK, was employed to re-evaluate the performance of the enhanced tMM algorithm. The original tMM algorithm correctly identified the vehicle's position 96.5% of the time; after enhancement this increased to 97.8%. This compares very well with the performance of tMM algorithms reported in the literature. The enhanced tMM algorithm developed in this research is simple, fast, efficient, and easy to implement. Since the accuracy offered by the enhanced algorithm is found to be high, the developed algorithm has potential to be implemented in real-time location-based ITS applications. Copyright © Taylor and Francis Group, LLC.

This paper explores the existing context of public transport provision in rural and remote areas illustrated with experience from Scotland. A critical review of existing Flexible Transport Services (FTS) in rural areas is provided and illustrated with selected case studies, with the objective of identifying the extent to which FTS can enhance the public transport offer. Findings confirm that FTS offers considerable potential to contribute to and support the public transport system in rural areas; however, the paper also identifies the many challenges in successful development or enhancement of FTS in rural areas.

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. © 2009 Elsevier Ltd. All rights reserved.

Public transport information provision in rural areas is often fragmented and of poor quality at best and non-existent at worst. This can have a significant impact on the everyday life of the inhabitants of rural areas, particularly in terms of limiting their travel choices and thereby their opportunities to access goods, service and social networks. Inadequate information provision also poses significant challenges during times of transport disruption. In this paper we examine the responses from a series of interviews (69) and focus groups (9) in which we explored the rural passengers’ experience during disruption, their coping strategies, and their behavioural responses to disruption. We identify that each passenger experiences disruption uniquely, and that the behavioral adaptation of the passenger relates to the severity and impact of the disruption. Furthermore, we identify that the most prevalent ways of mitigating the impacts of disruption is through time buffering and the use of kinship networks. Based on these findings and six co-design sessions with rural passengers we were able co-design and develop a prototype passenger information system to support the passenger during disruption. The results of this work aim to advance understandings of the interplay of technology, information provision, and passenger experience under disruption.

Passengers in rural areas are provided with little or no information regarding public transport disruptions. This can result in high levels of travel uncertainty with significant potential to affect travel behaviour. This paper, through 52 interviews, and 7 focus groups in rural areas in Scotland and England, explores the passenger experience, and the technology usage of individuals during disruption. The analysis indicates that a wide range of behavioural responses are evident, extending well beyond the choice of route or mode of transport. Further, we identify that the individual utilises various technologies (e.g. social media), and kinship networks to insulate against the effects of disruption. In addition, we present the co-design process of a set of technologies (a smartphone application and and an SMS service) that aim to improve the passenger exprience during disruption. This work provides an initial step towards understanding the interplay between disruption, passenger experience, and the design space for improving the passenger experience of individuals during disruption.

In this research, a passenger-centric passenger information system is proposed, which uses crowd sourcing and mobile phones so that passengers are not only information consumers but are also providers of information to the system. Passengers can allow the system to track their location through their smart phones when they are traveling on public transportation; this will compensate for the lack of a vehicle tracking system in public transportation, particularly in nonurban areas. Map-matching (MM) algorithms integrate location data (i.e.,latitude and longitude) obtained from positioning sensors (in this case, mobile GPS) with a digital geographic information system (GIS) road map. Any map-matching algorithm first identifies the road link on which a vehicle is traveling and then determines the vehicle's location on that road segment. In the proposed information system, at a given point of time, a number of vehicle locations (latitude and longitude) are received from passengers traveling on a bus. In order to provide a precise vehicle location at a given point of time, a novel map-matching algorithm using fuzzy logic, which integrates multiple vehicle locations (obtained from passenger's smart phones) with a GIS road map, has been developed. The developed map-matching algorithm was tested using real-world data collected on four different bus routes in Aberdeenshire, Scotland, and also GPS data collected in and around Nottingham, United Kingdom. It was identified that the developed MM algorithm is efficient and capable of supporting the proposed passenger information system. (C) 2013 American Society of Civil Engineers.

Real time passenger information (RTPI) is commonly available in urban areas for immediately before and during journeys; however, non-urban areas (e.g. suburban, rural, and remote areas) often have little or no access to it. One of the main challenges in providing RTPI in rural areas is the lack of infrastructure for obtaining real time vehicle location, and for providing information to passengers. In the Informed Rural Passenger Project, we attempt to address this problem through the use of passengers' smartphones. We have developed GetThere, a smartphone app that crowdsources travel information, including vehicle location, from the passengers. This information is then integrated using linked data principles with open data from various sources, and used by various web services to provide RTPI to travellers in rural areas. Initial evaluation of the GetThere system has been performed on urban, suburban, and rural bus routes in the North East of Scotland.