Principles-procedures and practices in road design

 SAFETY IN ROAD DESIGN

Principles

Accident reduction and accident prevention are the two main strategies in road safety.  The construction of the road network and the road design have a large effect on road safety. Accident prevention is the application of expertise in safe road design. This primarily involves road geometry as well as the materials used. A road is considered safe when there are NO ACCIDENT occurs. Below are listed a few important principles involved in design of safe roads.

• Physical separation of driving direction, separate cycle lanes
• Roads should be designed as per their functionality, i.e, service road, access road as a hierarchically structured road network
• The roads should be designed so that there is equality in speed, direction and mass at medium and high speeds
• The road should be designed such that it should minimize injuries in case of accidents by anticipating behavior of road users
• Roads and roadsides should be designed such that they are predictable and forgiving of mistakes
• The roads should be designed for travel speeds that suit the function and level of safety of the road
• The roads should be designed to prevent crashes and protect road users
• Provision should be made for the main supporting system elements like
• Emergency medical management
• Effective legislation and systems, enforcement and justice support systems
  

  Procedures

 Few of the procedures used by engineers to keep roads safe are

• Installing barriers
• Separate oncoming traffic
• Prevent vehicles from veering off the road and causing serious injuries
• Protect construction workers and pedestrians 
• Types of barriers
• Guard rail barriers
• Traffic cones
• Concrete barriers
• Steel cable barrier
  
• Setting speed limits for roads
• In April 2018, the Union Ministry of Road Transport and Highways fixed the maximum speed limit on 
• expressways at 120 km/h, 
• national highways at 100 km/h, 
• for urban roads at 70 km/h
  
• Installing traffic lights and signs at intersections and accident hotspots
•  An engineering solution for improving traffic safety performance is installing traffic lights
• Software specialising in road safety helps in identifying accident hotspots based on historical accident data
• Other techniques used for safe road design involve
• Repainting road markings
• Intersection channelization
• Drainage optimization and
• Sight distance improvement

Effectiveness of these solutions has been evaluated by Empirical Baye's (EB) models and an improved traffic safety performance has been observed by reduced crash occurrence risk and crash severeties.

 Practices 

•  Roads in India have a heterogeneous mix of traffic. Space occupied by each of these vehicles, acceleration and deceleration characteristics and possible maximum speeds by each user is variable. Hence, space allocation to different vehicles should be carefully ensured to ensure a smooth and safe flow of traffic.
• The type and character of each type of road (mobility or access or both)  should be carefully studied in detail. Appropriate geometric design standards is essential to ensure safety to all road users
• Design of entire road cross-section is important as it governs design speed of vehicles and reflects prioritization in space allocation and introduces the concepts of universal design and traffic calming
• Road markings are essential to guide road users and ansure a smooth flow of traffic
  

Road Safety Audit

 Road Safety Audit

Road Safety Audit (RSA) is a review of a project to assess and identify the safety concerns of road users. In this process, emphasis is laid on improving safety for vulnerable road users such as pedestrians and cyclists. 

RSA can be carried out in the following cases:

• To evaluate the safety of an existing road or an infrastructure.
• To identify the safety concerns of a proposed infrastructure during the planning, design and implementation stages.

Road safety audits are applicable to diverse types of road projects and to all categories of roads in both urban and semi-urban areas. Road safety audits should be conducted on all road projects, such as:

• Intersection design projects (signalized and non-signalized),
• Pedestrian and bicycle routes,
• Local area traffic management schemes in urban areas,
• Traffic calming measures in neighborhoods,
• Approaches to bridges, rail over/under bridges,
• Grade separators and interchanges,
• Implementation of Mass Rapid Transit System, etc.

The purpose of a road safety audit is to manage safety by identifying and addressing risks associated with road safety deficiencies. Auditing at different stages of a project, starting from the planning stage can lead to the timely elimination of problems and minimize time and costs of retrofitting roads/ transport infrastructure to improve safety at a later stage.
 

The benefits of conducting a road safety audit are:

• Identification of potentially unsafe locations along a road,
• Reducing the severity and likelihood of road accidents
• Reducing the need for costly remedial work by rationalizing the design,
• Minimizing the total cost of a project to the community by preventing accidents, disruption and trauma. 

The RSA process starts with the decision to build a new road, invest in reconstruction, widening or major maintenance of an existing road or simply to evaluate the safety aspects of an existing infrastructure. Road Safety Audits should be performed periodically since the planning stage of a roadway project, so as to ensure the safety aspects for all users are taken care at all the stages. It is recommended that RSA should be conducted in the following critical stages of a project life cycle.
Feasibility stage (if any new proposal is made on existing infrastructure)

• Design stage
• Construction stage
• Maintenance stage 

In the feasibility stage audit, the existing roadway where the project is proposed will be audited considering the safety aspects of the existing road. The results of any crash investigation, especially any previous road safety inspection reports must be considered in the feasibility stage for brownfield projects (i.e. retrofitting or maintenance of existing infrastructure). Feasibility stage audit need not be carried out for a greenfield project (a project where no transport infrastructure currently exists). The comments and suggestions noted after completion of the feasibility stage audit goes as an input to the design of the
proposed transport infrastructure.
 

Once the detailed design of the proposed infrastructure is completed, the design stage audit needs to be undertaken. The deficiencies identified in the design audit, if any, are to be addressed by making necessary changes in the design of the proposed infrastructure/facility.

The construction stage audit comes into picture when the project is under implementation after the approval of design drawings/ documents and the completion of the procurement process. The objective of this audit is to check whether adequate safety measures are taken during construction.

The final stage of the RSA process is termed as the monitoring stage. Monitoring stage audit needs to be carried out periodically during the service life of a project to ensure that the facility continues to serve road users in a safe manner. 

The list of people, responsible for Road Safety Audit at various stages of the project are:

• 
  

Operating Road Network for Safety Highway Operation and Countermeasures

Operating Road Network for Safety Highway Operation and Countermeasures

There always exists a scope for improving safety by rectifying earlier defective designs by better operational control and the application of accident reduction countermeasures. By selective use of traffic management and other techniques, it is possible to create safer, less congested and efficient traffic networks.

• Potential for conflict and accidents will exist wherever access is provided to roads carrying moving traffic and wherever roads intersect
• Safety will be improved if road users clearly and unambiguously understand which road has priority at intersections
• Pedestrians, cyclists and slow-moving vehicles (e.g. animal drawn) should be segregated from other moving vehicles
• Effective land-use controls can avoid many of the road safety problems which would otherwise occur with unrestrained development
• A safe road network is one where there is maximum differentiation between roads intended primarily for access and roads intended primarily for through journeys.

Problems

• Overloaded commercial vehicles
• Ineffectually wearing protective gear (Helmets/Seat belts)
• Problem locations (Congestion, Parking, Environmental nuisance)

Solutions

• Consultation  with
• town planning authorities
• local traffic safety organizations
• traffic police
• emergency services

• Publicity through
• Newspapers
• Television
• Radio
• Consultations with
• Local residents
• Shopkeepers
• Other stakeholders likely to be affected by the proposals

 

• Possible solutions / benefits
• Traffic  police enforcing traffic regulations
• Axle detectors linked to roadside equipment to monitor flow and enforce speed limit

BAYESIAN STATISTICS

BAYESIAN STATISTICS

• Frequentist Statistics tests whether an event (hypothesis) occurs or not. It calculates the probability of an event in the long run of the experiment Bayesian statistics is different from classical statistics
• Bayesian statistics is a mathematical framework to update your beliefs as you observe more data
  

For example, lets consider an accident

• Try recollecting the events that occurred just before an accident (This is called PRIOR)
• Occurrence of event (Accident) = DATA
• As more DATA is observed = BELIEF is UPDATED = POSTERIOR
• Accident is an event with TWO outcomes (YES or NO)
• Point data (YES or NO)
• Accident is a RANDOM VARIABLE (YES = X & NO = 1-X)
• RANDOM VARIABLE
• Start with the objective (Accident | Data)
• CONDITIONAL PROBABILITY ->  If PRIOR says there cannot be an accident, the BELIEF cannot be changed.
  

Baye's rule

• More data = More precise predictions
• Data is huge (difficult to compute) -> CHOP IT INTO PIECES (LARGE or unmanageable to SMALL CHUNKS or manageable pieces)
• Belief propagation in graphical models
  

Approximation -> Variational Bayes

• More data to update beliefs
• Use previous outputs as priors and subsequently update
• Belief influences results (Very subjective) similarly Prior is subjective
  

• Frequentists NOT INTERESTED in SINGLE EVENTS
• BAYESIAN statistics deals with UNCERTAIN EVENTS whereas FREQUENTIST statistics deals with REPEATABLE EVENTS
  

Bayesian statistics can:

• -incorporate prior knowledge easily
• -update beliefs easily
• -tackle a wider set of problems as probabilities are BELIEFS
• However, bayesian statistics MUST SPECIFY A MODEL
  

BELIEFs are SUBJECTIVE

Frequentist statistics 

• -has non-parametric methods
• -probabilities are objective
• -hard to cheat
• -are focussed on repeatable events
• -prior knowledge is introduced using and ad-hoc format
• -requires a huge data
  

FREQUENTIST and BAYESIAN statistics use the SAME RULES OF PROBABILITIES
Difference exists in set-up of WHAT IS RANDOM
Bayesian statistics uses UNCERTAINTY IN KNOWLEDGE
Frequentist statistics uses INTRINSIC RANDOMNESS
Usage of either methods is acceptable depending on DATA AVAILABLE and CONSISTENCY
BAYESIAN STATISTICS IS A FUNDAMENTALLY DIFFERENT APPROACH TO STATISTICS
It is an associated set of MATHEMATICAL tools
In BAYESIAN approach, DATA is FIXED and PARAMETERS may VARY
Frequentist statisticians talk about CONFIDENCE INTERVALS while BAYESIAN STATISTICIANS talk about CREDIBLE INTERVAL
 

Baye's theorem talks about

• -Posterion
• -Likelihood
• -Prior
• -Evidence
• Posterior = (likelihood * prior)/Evidence
• Strength of Prior
  

Usefulness of Baye's statistics in case of

• -Sparse data
• -Abundant data and
• -Uniform prior
  

Source of priors
 

Mathematical tools in Baye's statistics

• -Analytical methods
• -Grid approximation
• -Markov chain monte carlo simulation
• MCMC
  

-It is an algorithm for exploring parameter space
-Time spent at each point approximates parameter distribution
-Examples include Metropolis-Hastings, Gibbs sampling, etc
Bayesian methods perform extremely well in complex (hierarchical models)
Bayesian methods should be used in case of complex models with many interacting parameters
Bayesian methods are preferred when assumptions CANNOT be made regarding estimates and the data is messy (disorganised, missing data (gaps))
 

Bayesian road safety analysis

• Bayesian statistics for determining hazardous road locations
• Hazardous locations that are prone to traffic accidents are called "BLACK SPOTS". Identifying these black spots help in scheduling road safety policies. 
• A bayesian estimation of the model via a Markov Chain Monte Carlo (MCMC) approach is used. 
• Black spots are dangerous locations where accidents occur. Treating black spots is a well known and frequently used means of improving road safety. 
• Black spots are spatial concentrations of interdependent high-frequency accident locations. 
• From a statistical point of view, road accidents are treated as random events. As a matter of fact they are indeed unintentional result of human behaviour. \
• Hence, it is impossible to predict the exact circumstance of every accident. 
• There are several statistical models to analyse black spot data. 
• Most accidents follow the Poisson probability law. In order to correct the extra Poisson variation found in accident counts, binomial regression models are used. 
• Most recently, Bayesian techniques have been used to handle problems in traffic safety. 
• To estimate accident frequencies, a hierarchical bayesian poisson model is used.
• Identification of sites that are more dangerous than others (black spots) help in better scheduling road safety policies. 
• Bayesian estimation for the model using a Markov chain Monte Carlo is proposed. 
• The problem of identifying black spots is difficult since accidents are rare events and observed data is not necessarily a good indicator as it simply extracts data from an underlying density distribution.
• Policy making has a tremendous impact on society as it can reduce the accidents at a particular site.
• The hierarchical procedure for ranking sites  takes into account fatalities and injuries at all levels; combines this information by means of a cost function to rank the sites.
  

Application of computer analysis of accident data

 Computer Analysis of Accident Data

• Data collected from accident sites covers several aspects and is subjective to the person collecting the data. 
• Identifying the cause of road accidents is the aim behind accident data collection and reconstruction of the event with the main aim being reduction damages caused by traffic accidents.
• Because of exponential growth in population leading to increased number of vehicles on the road and consequently increasing accidents, the volume of data from accidents has reached explosive proportions.
• In order to manage this humongous data and analyse it to make sense to policy planners, data mining technologies are used
• 'WEKA' is a popular data mining program that can handle huge sets of data efficiently
• The results of data mining will help organizations such as transportation, to explore the accident data recorded by the police information system, discover patterns to predict future behaviors and effective decisions to be taken to reduce accidents.
• Road accidents are predicted through machine learning algorithms and advanced techniques for analyzing information, such as convolutional neural networks and long short-term memory networks, among other deep learning architectures. 
• Data sources for the road accident forecast is made. 
• A classification is proposed according to its origin and characteristics, such as open data, measurement technologies, onboard equipment and social media data.
• Road accident forecasting and Traffic accident prediction are driven by traffic engineering, data analysis and machine learning
• The main areas of interest of models obtained from computer analysis of accident data are 
• detection of problematic areas for circulation 
• real time detection of traffic incidents 
• road accident forecasting and 
• prediction of the severity of the consequences suffered by involved in a road accident  

• Therefore, the study of road accident prediction is a field of relevant and current scientific knowledge, open to innovation in the research of algorithms and data analysis techniques that respond to the challenge of generating a more secure mobility environment, which considers the pecularities of each country or region, i.e., traffic composition, weather conditions, roads conditions, and demography. 
• Data about accidents can be gathered by installing equipment on vehicles, for example satellite positional systems (GPS, GLONASS, Galileo), cameras and sensors, in order to gather data like acceleration, unexpected braking events, sudden lane changes and information about the driver behavior and status like drowsiness and level of stress
• Another emerging data source suitable for proposing models of road accident prediction is social media
• Government data like police bodies, traffic police and road concessionaires can be characterized as historical, since it contains data spanning several decades, and can be considered as reliable, because it is supported by the custody process of the entities responsible for the data.
• Open data can be defined, as the data that is produced and funded with public money, that is made available and accessible without restriction to the public .
• Road traffic information is usually one of the most available data.
• Measurement technologies include all kind of equipment that is part of the road infrastructure, such as radar, cameras, or equipment embedded on the road itself.
• By using analytic methods, researchers seek to characterize the information and variables of the road accident, in order to discover hidden patterns, profile behaviors, generate rules and inferences. 
• These patterns are useful to 
• profile drivers or drivers’ behavior on the road
• limit unsafe areas for driving
• generate classification rules related to road accident data
• perform selection of variables to be fetched in real-time model of accidents and 
• select relevant variables to be used to train other methods, such as artificial neural networks and deep learning algorithms. 

• Clustering is a method of partitioning and grouping objects into groups (clusters), so that objects grouped in each cluster share common characteristics, while looking for them to be clearly different from other objects grouped in other clusters. 
• Common characteristics can be interpreted as the level of correlation of objects according to the characteristics on which clustering techniques are applied.
• Unlike classification methods, clustering does not require that the data be previously marked with any particular category in order to distinguish different groups within the data. 
• The absence of these previous categories or classes indicates that the objective of clustering is to find an underlying structure in the information and achieve a more compact representation of it instead of discriminating future data into categories.
• The main advantages of clustering algorithms are that they do not require prior data processing, work well with large data sets, and their results can be interpreted graphically. 
• On the other hand, clustering algorithms are sensitive to the possibility of finding a local maximum instead of a global maximum on their optimization functions.
• Clustering algorithms use a distance function to calculate the similarity in characteristics when they work with continuous elements and a measure of similarity for data with qualitative elements. 
• Among the techniques based on similarity functions we can include K-nearest neighbor and K-means clustering
• Cluster techniques whose similarity function is based on distribution probabilities, their operation is based on the premise that each cluster has an underlying probability of distribution from which the data elements are generated. An example of this type of algorithm is latent class clustering (LCC)
• For data sets with attributes both qualitative and quantitative, clustering techniques such as two-step clustering
• Batch clustering, in combination with fuzzy C-means and real time clustering is used to study abrupt braking events in real time
• Batch clustering results, correlations were obtained that indicate potentially dangerous places for driving, according to the time of day.
• K-means clustering and association rules model in order to determinate the variables that influence the event of road accidents, obtaining a 6-cluster model, which was used as an input to a rules association model. 
• It was found by computer analysis of accident data that accident severity, type of road, lighting present in the road and the type of surrounding area were important factors in any accident
• Real-traffic data is used in order to predict the number of accidents on any road or intersection and to identify risk factors using clustering to group roads and finding risk patterns. 
• The quantity of clusters was evaluated and selected using the Bayesian information criterion (BIC)
• A decision tree builds classification models in the form of trees or dendrogram, each node represents one of the input variables, and each node has several branches equal to the number of possible values of said input variable. 
• Decision trees are useful tools in pattern classification applications.
• Decision tree method of analysis is exploratory and not inferential.
• Rule learners and classifiers do not require prior data processing and work well with large data sets and rule learners and classifiers can be interpreted graphically; however, their results are not as accurate
• Road Accident Data Management System (RADMS) is a Geographic Information System (GIS) based software that is funded by world bank used for collecting, comparing and analyzing road accident data.Currently, it is being used by the government of Tamil Nadu.
• RADMS is a comprehensive traffic-management system which helps to study and analyse traffic accidents in a scientific manner.
• The various components of RADMS are:
• Creation of GIS database
• Web based access and data flow
• Report generation and plotting results on maps
• Analysis and identification of black-spots for  police and transport departments to take-up necessary measures
• RADMS generates the following twelve types of reports for analysis and suggestion of remedial measures
• Driver report
• Vehicle report
• Road report
• Yearly report
• Enforcement
• Collision type
• Time period report
• Alcohol usage report
• Person report
• Landmark report
• Weather report
• General report
•  RADaR is a robust road crash database in order to reduce road accidents. RADaR is Road Accident Data Recorder
• RADaR is an end-to-end solution for road accident data recording and reporting as it helps identify the factors contributing to road accidents
  
• RADaR is designed as a n application for android tablet with connectivity to web-based database server.
• It used GPS/GPRS to record exact accident location in global coordinate system and transmits data to web-based central server
• It also provides a facility to take photographs of the accident scene and upload it to the network
• It features a pictorial menu-driven recording of road layout of crash site and collision diagram plotted on layout for scientific investigation
• RADaR can draw data for vehicle registration and driver license information from national databases
• The pilot studies for RADaR was carried out in New Delhi (India) and Addis Ababa (Ethiopia)
• AI machine-learning method is used to create decision trees distinguishing the characteristics of accidents
• In order to identify factors causing accidents, Data Mining (DM) techniques such as Decision Trees (DTs) that are used as they allow certain decision rules to be extracted. These rules could be used in future road safety campaigns thereby enabling managers to implement priority actions.
•  Artificial Neural Network (ANN) models are used for the analysis and prediction of accidents. In this technique, the number of vehicles, accidents, and population are selected and used as model parameters. The sigmoid and linear functions are used as activation functions with the feed forward-back propagation algorithm.
• The ANN model has demonstrated to be better than statistical methods in use.
• Since the data collected from accident sites is huge, it falls under the domain of 'BIG DATA'. 
• Traffic on highways is monitored and lots of data is processed daily to predict probability of accidents based on highway conditions like road surface, light on highway, turns etc. 
• Accident prediction is based on different queries and in order to process this big data, Hadoop has been used. 
• Execution time is very less on Hadoop as compared to other sequential techniques.
  

Empirical Baye's method of identification of hazardous road location

 Empirical Baye's method of identification of hazardous road location

• An empirical method involves the use of objective, quantitative observation in a systematically controlled, replicable situation, in order to test or refine a theory.
• Empirical is something that is based completely on experiments or experience. It is the opposite of theoretical derivations which are based on theories developed in the past.Additionally, theory is constrained by assumptions and conditions
• The Empirical Baye's (EB) method increases the precision of estimates when the data is limited and corrects the regresion-to-mean bias.
• Currently, it is used in Comprehensive Highway Safety Data Model (CHSDM)
• The safety of a road is estimated by the number of accidents expected to occur in a specified period.
• If safety estimates are based on accident counts, the data may be too imprecise to be useful
• The Empirical Baye's (EB) method for estimation of safety increases the precision for estimation and corrects for regression-to-mean bias
• The EB method should be used whenever the need to estimate road safety arises
• The expected accident frequency at similar roads is determined by Safety Performance Function (SPF).
• Determination of sites that are more dangerous than others helps in better scheduling road safety policies
• Bayesian estimation for the model via a Markov Chain Monte Carlo (MCMC) approach is used
• Methods that can measure and produce comparable results concerning the risk of each site are of special interest for designing new roads or to enforce rules.
• This implies the existence of criteria that consider a particular site as hazardous
• Black-spots are dangerous locations where several accidents occur
• Treating black-spots is a well-known method of improving road safety
• From a statistical point of view, road accidents are treated as random events
• Accident count follows the Poisson Probability law. However, to correct Poisson variation in accident counts, negative binomial regression models have been used.
• Recently, Bayesian techniques have been used to tackle problems in traffic safety
• Data forms the basis for using the EB method. Hence, the previous data about hot spot accident sites on a stretch of a road are selected.
• The accident sites are classified according to their localization.
• The total number of accidents at the locations are identified
• At each site i, the number of accidents Xi is counted
• The injuries are classified into fatalities (Yi), heavily injured (WIi) and light injuries (Zi)
• Considering ti = 1 for all i=1........n the cost of accidents at a particular site (Ci) is given by       

Ci = E(Yi) + 0.075E(Wi) + 0.0035E(Zi)

E = Expenses related to a death or injury

• The problem of ranking sites is a difficult one since accidents are rare events and observed data are not necessarily a good indication as they extract data from an underlying density distribution
• Currently, the hierarchical bayesian procedure (Empirical Bayesian method) for ranking hazardous sites takes into account not only fatalities but also injuries (both light and severe) and combines this information by means of a cost function in order to rank the sites.
• It follows logically that the sites incurring the maximum cost are identified as hazardous road locations.
  

Statistical methods of analysis of road accidents

STATISTICAL METHODS OF ANALYSIS OF ROAD ACCIDENTS

The following statistical methods are used for analysis of road accidents

Logistic regression method

• Logistic regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. 
• The outcome is measured with a dichotomous variable (in which there are only two possible outcomes).
• Logistic regression is a predictive analysis. 
• Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables. 
• When selecting the model for the logistic regression analysis, an important consideration is the model fit.
• Adding independent variables to a logistic regression model will always increase the amount of variance explained in the log odds.  
• However, adding more and more variables to the model can result in overfitting, which reduces the generalizability of the model beyond the data on which the model is fit. 
• The statistical model of linear regression is the most popular technique in accident severity research because the relationship between accidents and correlated factors can be clearly identified.

Evans pair and double pair comparison method

• This is one of the most popular techniques in the road traffic community for assessing the risk factors on the probability of death in a road traffic accident. 
• This approach is not model based and is not explicit about the assumptions concerning relative relative risk. 
• It uses the concept of multiplicative relative risk. 
• The double pair comparison method uses two such quantities. 
• In both methods, Evans bases variance approximations on an assumption of Independent poisson processes.
  

Greenlands method

This method is based on the assumption of multiplicative relative risks and was proposed by Greenland (1994). Unlike Evans (1985) it explicitly makes model assumptions. It is also a true regression technique rather than a stratified approach.Greenland method is desirable since it enables the estimation of the relative risk reduction or case load reduction when a risk factor is changed. However, since the method assumes multiplicative relative risks, the same reservations are expressed about the unsuitability of this assumption for the range of  probabilities encountered in the road traffic area also hold for this new method.Greenland's method is recommended over the methods of Evans (1985).

The Greenland's method is 

• based on sound contemporary statistics,
  

• the model assumptions are explicit,

• it is a true regression technique,

The theory for statistical inference is readily available.

Conditional logistic regression

• It is one of the most under-utilized statistical techniques in the road traffic literature. It finesses the problem of truncation and enables a regression analysis of the FARS database.

• It is a very accessible technique which has been available in the statistical literature for many years.
  

• The technique is available in most modern statistical packages such as SAS, S-Plus and EGRET.
  

Truncated logistic regression

• In such cases, data is recorded in accidents where atleast one death occurred. 
• Databases of such type tend to be under-reported as there exists a tendency not to report accidents where damage and injury is minimal.
• Application of logistic regression without any correction for truncation yields an exaggerated estimation of the relative advantages of one group over the other in terms of survival. 
• The Truncated Logistic Regression (TLR) approach conditions on the probability that an accident is observed which is the probability that it results in at least one fatality. 
• Advantages of truncated Logistic Regression
• -Since the TLR uses the full information from the sample it can be expected to lead to more accurate inference than CLR or DPC.
• More effects can be fitted using TLR. The conditional logistic regression likelihood
  equation only includes terms which vary within a given accident. For
  example, for single vehicle accidents, since the speed of the car is constant for
  all the occupants, its effect on the survival prospects cannot be estimated using
  CLR.
• TLR on the other hand can be used to estimate its effect.
• TOR can be used to estimate the relative seriousness of crashes for occupants.
• The TLR method allows us to estimate the probability that a given type of crash
  will kill a given type of occupant. 
• The TOR method enables the estimation of the probabilities of the various categories of injury.
• Only TLR can be used to estimate the total number of potentially fatal crashes.
• The TLR method allows us to estimate the probability that a particular configuration
  of factors results in a fatality. 
• By dividing the observed number of crashes of this type by this probability we obtain an estimate of the total number of potentially fatal crashes of this type. 
• The estimates can then be summed over the categories of crashes to obtain an estimate of the total number of potentially fatal crashes.
• TOR can be generalized to different link functions. 
• The TOR method allows us to choose the link function which best fits a given data set.
  
  Bayesian Techniques
  In very recent years, there has been great research interest by statisticians in a variety
  of Bayesian techniques. 
• The Bayesian paradigm assumes that the unknown parameters are themselves random variables which have a distribution called a prior.
• The prior is chosen by the researcher before the data is collected to represent the
  researcher’s prior belief about the unknown parameter. 
• The primary objection to Bayesian techniques has been that the choice of the prior is subjective.
• Different researchers will have different priors and consequently arrive at different conclusions
  from the same set of data. 
• This problem can be averted by choosing non-informative priors which essentially assume that the researcher has no apriori knowledge about the unknown parameter. In this way the classical frequentist inference can be mimicked.
• However the methods can be applied to a variety of problems that are intractable
  using conventional statistical inference.
• The Bayesian method proposes a distribution for the unknown total number of accidents and the distribution of the covariates in those accidents. 
• The parameters of those distributions are themselves given a distribution, called a prior.
• Then the probable distribution of the parameters given the observed data, called the posterior, is calculated. 
• From the posterior various quantities such as confidence intervals can be calculated. 
• The mathematics involved in calculating the posterior is often intractable since it will often involve high dimensional integrals. 
• That is the case when truncation is involved. 
• Gibbs sampling is a technique which allows us to obtain estimates in such situations. 
• Gibbs sampling and variants such as the Metropolis-Hastings algorithm are methods that generate observations from the posterior distributions and use the resulting data to perform inference. 
• As a hypothetical example suppose we wished to estimate the total number and
  character of accidents in rural areas using data from a truncated database. The Gibbs
  sampling technique would use the data from only accidents with a fatality and would
  give confidence intervals for the total number of accidents and the overall pattern of
  accidents.
• Gibbs sampling is one example of a set of modern statistical simulation techniques
  which are designed to explore likelihoods and posterior distributions. 
• The methods are very computational and have only become feasible for general use in the last few
  years.  

TOR - Technique of Operations Review

Bayesian Statistics

• The concept of probability has been introduced in mathematics to deal with uncertainties.
• Probability in mathematics represents a set of reasonable values. 
• The relationship between probability and information is given by Baye's rule. 
• Inductive learning through Baye's rule is called Bayesian Inference.
• Bayesian methods are data analysis tools that are derived from the principles of Bayesian inference.
• Bayesian methods are useful in road safety engineering as they predict missing data and forecast future data. They provide a computational framework for model estimation, selection and validation. 
• Bayesian methods are used for a variety of inferential and statistical tasks.
• Statistical induction is a process of learning about the general characteristics of a population from a subset of members of that population.
• Numerical values of population characteristics are expressed in the form of a parameter theta, and numerical descriptions of the subset form a dataset y. 
• Before a dataset is obtained, the numerical values of the population characteristics and the dataset are uncertain.
• Subsequently, after obtaining the dataset, the uncertainty of the population characteristics decreases.
• Quantifying this change is the purpose of bayesian inference. 
• Sample space is the set of all possible datasets. 
• Parameter space is the set of possible parameter values from which one value best represents the true population characteristics. 
• Ideal bayesian learning consists of a numerical formulation joint beliefs about dataset and parameters that are expressed in the terms of probability distributions over samplespace and parameter space. 
• Prior distribution
• Posterior distribution
• Population characteristics
• Sampling model
• The posterior distribution is obtained fro prior distribution and sampling model through Baye's rule:
• Baye's rule does not provide us with answers. However, it changes our outlook in the context of new information. 
• Baye's rule is an optimal method about updating beliefs about parameters when given new information from a dataset. 
• Thus, there is a strong theoretical justification for the use of Baye's rule as a method of quantitative learning.
• The Empirical Bayes method addresses two problems of safety estimation; it increases the
  precision of estimates beyond what is possible when one is limited to the use of a two-three year
  history accidents, and it corrects for the regression-to-mean bias.
• The theory of the Empirical Baye's method is well developed. It is now used in
  the Interactive Highway Safety Design Model (IHSDM) and will be used in the Comprehensive
  Highway Safety Improvement Model (CHSIM).
• Safety can only be estimated, and estimation is in degrees of precision. The precision of an estimate is usually expressed by its standard deviation.
• The safety of entities on which many accidents occur during a short period can be estimated
  quite precisely by using only accident counts.
• When it takes a long time for few accidents to occur, the estimate is imprecise.
• The important shortcoming of safety estimates that are based on accident counts only is that they are too imprecise to be useful.
• Another disadvantage of safety estimates is that are based only on accident counts which leads  to them being subject to a common bias.
• The existence of this ’regression-to-mean’ bias has been long recognized; it is known to produce inflated estimates of countermeasure effectiveness.
• Rational management of safety is not possible if published studies give rise to unrealistic expectations about the effectiveness of safety improvements.
• The Empirical Bayes (EB) method for the estimation of safety increases the precision of
  estimation and corrects for the regression-to-mean bias. 
• It is based on the recognition that accident counts are not the only clue to the safety of an entity.
• Hence, not only accident counts but also knowledge of the typical accident frequency on similar roads is to be considered.
  

Analysis of road accidents to arrive at real causes

ANALYSIS OF INDIVIDUAL ACCIDENTS TO ARRIVE AT REAL CAUSES

Accident analysis is carried out in order to determine the cause or causes of an accident  so as to prevent further accidents of a similar kind. It is part of accident investigation. These analyses are carried out by  forensic scientists, forensic engineers or health and safety inspectors. It is retrospective nature meaning that accident analysis is primarily an exercise of directed explanation; conducted using the theories or methods the analyst has on hand, which directs the way in which the events, aspects, or features of accident phenomena are highlighted and explained.

Accident analysis is performed in the following stages-

• Fact gathering
• Expert analysis
• Drawing the conclusion
• Counter-measures

Fact gathering is a forensic process involving in order to gather all  relevant facts that contribute towards understanding of the accident

Expert analysis is carried out once the fact gathering process is completed and it heavily depends on the knowledge and experience of experts involved. The drawback of this stage is that it simply puts the facts together to give a big picture. In this step, the accident is reconstructed and checked for consistency and reason-ability.

The different types of accident analysis methods are

• Causal analysis which uses the principle of causality to determine the course of events. 
• Expert analysis involving knowledge and experience of field experts
• Organizational analysis which relies on systemic theories of organisation. 

Several models have been described to characterise and analyse accidents

Photographs are also used to extract evidence. Accident crime investigators and law enforcement officers use techniques like 'camera matching', 'photogrammetry' and 'rectification' to determine the exact location physical evidence shown in accident scene photos.

Data collection in the event of a road accident

DATA COLLECTION IN CASE OF A ROAD ACCIDENT

 A crash involves the participation of 3 factors 

• Human
• Vehicle and a 
• Specific environment (infrastructure). 

An accurate understanding of the crash occurrence demands identifying the failures in each of these three factors over 3 time phases 

• Pre Crash
• Crash and 
• Post Crash. 

In-depth crash data collection methodology covers 

• Detailed vehicle inspection
• Crash scene inspection and 
• Witness/victim interviews for identification of evidence across all the three factors and phases, documented in the form of photographs, measurements and data forms.

Crash Scene inspection

Inspection involves identifying any traces left on the road as a result of the crash, understanding the available road geometry, road furniture, markings and signage, and finally, measuring and mapping the complete crash scene to create a to-scale scene diagram.

Vehicle inspection

Inspection covers mapping and measuring the external damages on the vehicle, inspection of interiors to document safety system availability / usage, measure the integrity of the passenger compartment and occupant contacts resulting in injuries.

Victim/Witness interviews

Interviews focus on questions targeted at extracting the pre-crash situations, sequence of events and any relevant information related to the crash. Witness/victim statements are subjective and data from such interviews are always validated against the evidences from vehicle and crash scene inspection.

 The reconstruction of a crash gives a realistic model of the event by adopting a systematic approach. The data collected at accident site is used as input and used to develop a computer simulation of the event.

The steps that are followed for crash analysis and reconstruction are as follows:

• Investigation of the crash site
• Measurement of details on the crash site
• Collection of data
• Making Scaled drawings
• Modeling of vehicles, pedestrians and vehicle – occupants
• Computer simulation and optimization

Scientific investigations of road accidents

SCIENTIFIC INVESTIGATION OF ROAD ACCIDENTS 

• The scientific investigation of accidents is a branch of forensic science. Forensic scientists will carefully examine the scene of a crime for physical evidence which can be subjected to analysis. 
• The outcome of such analyses may assist the Court to determine guilt, innocence, fault or liability. Accident Investigators carry out their work in exactly the same way.
• In road traffic accidents the primary physical evidence lies in the site details, the conditions which prevailed at the time of the accident, the state of the road, marks and debris on the road, the physical characteristics of the vehicles involved, the damage sustained by the vehicles, the injuries sustained by persons involved in the accident, the police plan, the police measurements, the police photographs, the police video and in the laws of physics which determine the movement of vehicles, cyclists and pedestrians before, during and after an accident. 
• Secondary physical evidence comes from the statements of witnesses when they refer to times, distances, speeds and locations. However, the validity and interpretation of such statements are matters for the Court to determine. 
• Statements which refer to physical parameters will sometimes have additional physical implications.
• India, as one of the signatories of the Brasilia Declaration, is committed to halving road accident deaths by 2020.
• The first step in scientific investigation of road investigation involves isolating the true causes of death on roads and rectifying the same.
• Accident investigations inform future safety improvements, and prevent and prepare for future accidents.
• Road accident investigation should be a robust iterative system, which follows a constant feedback loop
• The Motor Vehicle Amendment Bill 2017 through an amendment allows the central government to formulate schemes that facilitate in-depth analysis of road accidents.
• The Accident Investigation Report (AIR) is too brief to be of any real use. The Detailed Accident Investigation Report (DAR) carries more information such as, a site plan drawn to scale describing skid marks, nature of traffic on the road and several other details, photographs of the accident site and vehicles from ‘all’ angles, driving conditions at the time, road surface conditions, details of road encroachments at the site, driver’s condition, victim’s injury and a mechanical inspection of the vehicle which includes, mechanical inspection of the vehicle, condition of tyre treads, air bags, and point of impact and level of damage. 
• RASSI (Road Accident Sampling System - India), a relatively new initiative, aims to create a road traffic accident database through on-site crash investigations helping governments, citizens and the industry make informed data-led decisions about improving road safety in India.
• RASSI database is based on international databases such as the US National Automotive Sampling System – Crashworthiness Data System (NASS-CDS), the German In-Depth Accident Study (GIDAS), and the UK’s Co-operative Crash Injury Study (CCIS), but it has been designed and developed to reflect Indian conditions.
• Scientific investigation of road accidents is therefore a key component of any policy directed at reducing road accidents.
• Road Accident Sampling System-India (RASSI), is a repository of accident data of over 3,750 accidents investigated across India over a period of 7 years.
• It is a pioneering Indian initiative aimed at collecting in-depth scientific road accident data, through on-site crash investigations, that will enable the government, industry and other road safety stakeholders to plan and execute data-driven road safety strategies for safer Indian roads.
• Forensic Crash Investigation is a quick consultation to understand an accident or full-fledged investigations to reconstruct and understand injury causation.
• It is used to deliver easily understandable and evidence based explanations of the sequence of events leading up to any road traffic accidents.
• Special crash investigations examine unique real-world crashes anywhere in India and perform a detailed examination in a timely manner that can be used by the safety community to understand, evaluate and improve the performance of existing safety systems.

Causes of road accidents

INTRODUCTION

• India has the dubious distinction of a very poor safety record

• More than a lakh people get killed on India's roads every year

• Engineering,  Enforcement and Education are the three E's of safety

CAUSES OF ROAD ACCIDENTS

• Some of the common errors due to human beings resulting in road accidents are as follows
• Over speeding, rash driving
• Drunk driving
• Distracted driver
• Jumping red signal
• Avoiding safety gear like helmet and seat belt
• Violation of rules
• Failure to understand signs
• Fatigue
• Pedestrians contribute to road accidents by
• Carelessness
• Illiteracy
• Crossing at wrong places
• Moving on the carriageway
• Jaywalking
• Passengers of vehicles cause accidents by
• Projecting their body outside the vehicle
• Talking to drivers
• Alighting and boarding vehicle from the wrong side
• Travelling on foot boards
• Catching a running bus, etc
• Vehicles
• Failure of brakes or steering
• Tyre burst
• Insufficient headlights
• Overloading
• Projecting loads
• Road conditions
• Potholes
• Damaged road
• Eroded road
• Merging of rural roads with highways
• Diversions
• Illegal speed breakers
• Weather conditions
• Fog
• Snow
• Heavy rainfall
• Wind storms
• Hail storms
• Direct consequences of accidents
• Death
• Injury
• Property damage

Important keywords, points to remember

• Methodology for conducting accident-causation research / model / experimental projcts
• .Case study approach
• Reconstruction
• Probabilistic model
• Statistical inference approach
• Multiple regression model
• Multi dimensional contingency tables
• Computer programs to analyze data from field experiments
• Traffic safety research topics are multidimensional with several levels of many of the variables. Hence a multidimensional contingency approach is preferred
  
• Statistical approach is useful as it can handle complex, interactive phenomena in an objective manner. It allows isolation and study of individual factors
• Statistical approach is preferred over case study approach
• Accident patterns help prediction and computer simulation / modelling
• Conceptual framework for accident related factors is very useful in prediction
• Factors are classified as sequential or operational and simultaneous
• Operational failures are functional failures. Examples are-
• Malfunctioning of perception
• Decision performance in trip planning
• Driving strategy or evasive tactics
• Examples of condition factors are-
• Deficiencies in basic attributes of roads, vehicles and people

• Accident is not a single event. It is a process
• No single factor can explain the accident
• Causal links, each preceded by another
• Each preceding link has lesser relevance to the accident
• By using the above four points, thousands of accidents can be studied by computer analysis (PERCHONOK)
  
• The logic involves division of accident generation process into its component parts and provide an exhaustive list of mutually exclusive items for each of these parts. The resulting series of checklists is called CAUSAL STRUCTURE
• The top level breakdown of causal factors is-
• Human direct causes
• Human condition and state
• Environmental factord
• Vehicular factors

Traffic safety experiment

• A factor F is said to be the cause of accidents if the conditional probability of an accident occurring in the presence of F is greater than the conditional probability of an accident occurring in the absence of F, as determined under condition set E.

• Features of case study approach
• Transient factors
• Dozing
• Lack of attention

• Human factors
• Monocular vision
• Color blindness
• Chronic disease states

• Limitations
• Vehicle handling
• Braking
• Steering
• Human detection
• Perception
• Decision
• Reflexes
• Tinted windshield
• Visual capabilities of driver
• Ambient light levels
• Reflectiveness of different objects

• Features of statistical inference approach
•  Non-accident information
• Accident causation factors
• Stability of factors
• Alcohol
• Loss of consciousness
• Inadequate search
• Detection / perception failures
• Recognition failures
• Decision failure
• Vehicular and environmental failure
• Tire blow-out
• Brake failure
• Complete loss of steering
• The above three cases should be studied as CASE-STUDY and NOT as STATISTICAL INFERENCE method

• Examples of Environmental problems are
• Glare from oncoming headlights
• Smooth road surface due to freezing conditions
• Threaded tires vs Bare tires

INDEX

ROAD SAFETY ENGINEERING

UNIT-I

Road Accidents:

• Causes, scientific investigations and data collection
• Analysis of individual accidents to arrive at real causes
• Statistical methods of analysis of accident data
• Basic concepts of Road accident statistics,
• Bayesian concepts of road accident analysis
  

Safety performance function: 

• Bayesian Statistics
  
• The empirical Bayes method Identification of Hazardous road location.
• Application of computer analysis of accident data.

Road Safety Engineering Problems

UNIT-II

Safety in Road Design

• Operating the road network for safety highway operation and counter measures
• Road safety audit
• Principles-procedures and practice
• Code of good practice and checklists
• Vehicle design factors 
• Driver characteristics influencing road safety.

UNIT-III

Road Signs and Traffic Signals:

• Classification
• Location of Signs
• Measures of sign effectiveness
• Types of visual perception
• Sign regulations
• Sign visibility
• Sign variables
• Text versus symbols. 

Road Marking

• Role of Road markings
• Classification
• Visibility. 

Traffic Signals

•  Need
• Signal face
• Illumination and location of Signals
• Factors affecting signal design
• Pedestrians’ safety
• Fixed and vehicle actuated signals
• Concepts of design of traffic signals
• Problem on design of traffic signal
• Design of traffic signal at intersection - Problem
  
• Solutions to problems on design of traffic signals

Area Traffic control

• Delineators
• Traffic Impact Attenuators
• Road side rest areas
• Safety Barriers
• Traffic Aid Posts.

UNIT-IV

Traffic management techniques

• Integrated safety improvement and Traffic calming schemes
• Speed and load limit
• Traffic lights
• Safety cameras
• Tests on driver and vehicles
• Pedestrian safety issues
• Parking
• Parking enforcement and its influence on accidents
• Travel demand management
• Methods of traffic management measures
• Restriction of turning movements
• One way streets
• Tidal flow operation methods
• Exclusive bus lanes and closing side streets
• Latest tools and techniques used for traffic management
• Road safety issues and various measures for road safety
• Legislation
• Enforcement
• Education and propaganda
• Air quality
• Noise and energy impacts
• Cost of road accidents

UNIT-V

Incident management

• Introduction
• Characteristics of traffic incidents
• Types of incidents
• Impacts
• Incident management process
• Incident traffic management
• Applications of ITS
• Motorist information Systems
  
• Equipment used for incident management
  
• Planning effective incident management program
• Best practices in incident management programs
• National importance of survival of transportation systems during and after all natural disasters especially cyclones, earthquakes, floods etc. and man-made disasters like sabotage, terrorism etc

Important points / Keywords to be remembered