Information System or GIS?

It is a system designed to l capture, l store, l manipulate, l analyze, l manage, and

l present spatial or geographic data.

Information System or GIS?

l A GIS uses spatial location as the key index variable.

l Spatial location is used to relate seemingly unrelated information l To find out patterns and trends l That are not so obvious l When information is trapped in charts and spreadsheets

Information System or GIS?

Locations or extents in the Earth space–time may be recorded as:

l dates/times of occurrence, and

l x, y, and z coordinates representing, longitude, latitude, and elevation, respectively.

All Earth-based spatial–temporal location and extent references should be relatable to one another and ultimately to a “real” physical location or extent. This key characteristic of GIS has begun to open new avenues of scientific inquiry.

The Link Between Traffic Engineering and GIS

l Traffic Engineering aims to achieve a safe and smooth flow of traffic

l Traffic is a parameter that changes with both time and space

l Therefore both real-time and non-real-time monitoring and analysis of traffic related variables is required

l GIS with its capability to incorporate techniques to analyze spatio-temporally variable data serves as the perfect tool for

Traffic Engineering

Applications of GIS in

Traffic Engineering

A few major applications presented here are:

lRoad Network and Public Transportation Route Planning (during preliminary design and for further management once in use)

lFastest Route Planning (to save time during emergencies)

lAccident Hotspot Analysis

lParking Demand Solution

lTraffic Induced Pollution Study and Control

lTraffic Rule Violation Reduction by realtime monitoring

lTracking of Public Transport and Availability

lToll Collection System Management

lUtility Location Identification

Route Planning

No of trips required, distance involved

No of public transport vehicles required, cost involved

Projected Demand in Future

Total Population, No of Households, Age of Population

Private Vehicle Ownership, Income Catagory

Percentage of Population with Disability

No of Employees

Current transit riders

People interested in Transit

Route Planning

After planning of the route by considering the above criteria, the implementation of the route involves:

lDeciding the terminal points and stoppages for the public transport. The boarding points should be distributed according to the demand area densities shown in the map.

lThe effect of the boarding points on the existing traffic flow should also be taken into account by using realtime modelling and simulations.

lThe next step after implementation of boarding points is monitoring them. Every data regarding the boarding point like the location id,the number of bays, the passenger facilities provided are fed into the GIS for further monitoring and analysis of performance.

Finding out the Most Efficient

Route

Direct applications of finding out the most efficient route to a particular destination are as follows:

lTo find out the fastest route to a hospital or other utility centers during an emergency

lTo find out the most efficient delivery routes utilising minimum number of vehicles and maximising number of delivery and pick up points per vehicle

lOn a daily basis,navigating the streets to reach a destination on an app based cab service

Finding out the Most Effcient Routes

l The basic principle of this problem lies in graph theory

l It states that the shortest path is the path between 2 vertices or nodes in a graph such that the sum of the weights of its constituent edges is minimized.

l We can imagine the road network as a graph with positive weigths, and the nodes represent road junctions and each edge of the graph is associated with a road segment between 2 junctions.

l The traffic equivalent of this problem is finding the shortest path between 2 intersections on a road map each weighted by the length of its road segment.

l The weight of an edge may also correspond to the time needed to traverse the segment, or the cost of traversing the segment.

Finding out the Most Efficient Route

l There are several standard algorithms to solve the above problem like: Dijkstra’s Algorithm, Bellman-Ford Algorithm, and more specialised ones like The Travelling Salesman Problem.

l The problem with the direct usage of these is that they assume the road network conditions to be static, whereas traffic and therefore the weights of the routes, fluctuates with time.

l A traveller traversing a link daily may experience different travel times on that link due not only to the fluctuations in travel demand ( origindestination matrix) but also due to such external factors like work zones, bad weather, accidents, and vehicle breakdowns.

l As a result a stochastic time dependant modelling and solution is more suited for optimisation of the shortest path problem in traffic. Therefore the shortest route might not be the fastest.

l A GIS system with continous shared dataflow system that will collect the changing traffic data at every point of time from the traffic signals and detectors is required to update the constraints for the algorithm.

l The shortest path therefore needs to be modified with time and change in traffic.

Finding out the Most Efficient Route

Accident Data Verification and Hotspot

Analysis

l An accident hotspot may be loosely defined as a high density accident zone.

l There are in built functions in GIS softwares to find out the spatial autocorrelation between accident site locations.

l The Marvin’s I method is used to find out whether the accident locations in an area, in terms of distance, forms a cluster pattern or a dispersed pattern or a random pattern. It uses both feature locations and feature values simultaneously.

l To quantize the spatial locations into clusters, K-means clustering is a standard methods that is used. It aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster.

Accident Data Verification and Hotspot

Analysis

l There is also the Kernel Density Estimation technique. A symmetrical surface is placed over each point. Then the distance from the point to a reference location is evaluated based on a mathematical function and then the value is summed for all the surfaces for that reference location. This method is repeated for successive points. This therefore allows us to place a kernel over each observation and summing these individual kernels gives us the density estimate for the distribution of accident points.

l The main advantage of this method lies in the determination of the spread of risk of an accident. The spread of risk is defined as defined as the area around a cluster where there is an increased likelihood of occurrence of accident based on spatial dependency.

l After determination of the clusters of hotspots, further analysis can be made on causes of accident and measures taken based to reduce the risk.

Parking Demand and Supply Analysis

l Parking demand forms the link between landuse and traffic engineering

l Supply to meet the parking demand can solve a majority of transport problems including congestion and travel time reduction

l Major problem in analysis is the variable nature of the demand and the data associated with it, which requires detailed survey

l To meet the demand, supply

locations are allocated by GIS softwares as per locationallocation techniques and

Fig 5:Parking Demand and Supply Analysis models

Parking Demand and Supply Analysis

l Location-allocation models aim to determine optimal locations for facilities based on demand distribution. It minimizes the total weighed distance or time in terms of demand locations and weights.

l The models are developed with GIS technology because it needs network analysis functions. The location allocation problem has three basic components which are facility, demand, and network space explained as follows:

l Facility is chosen such that when it will be analyzed to service demand, it is the best in terms of distance and time. The capacity of the facilities can be used for analyzing demand and supply balance or for allocating demand weights.

l Demand is determined as a result of parking demand analysis. Demand locations are identified with demand weights.

l Network space is the base data set on road network to calculate distance or time cost between facilities and demands.

l The p-median model and the coverage models are the most commonly used methods chosen for location-allocation analysis.

Parking Demand and Supply Analysis

Monitoring and Reduction of Vehicular Air

Pollution

l Vehicles are one of the major sources of air pollution. They serve as mobile sources which makes them difficult to monitor.

l Ways of tackling air pollution levels around the world, especially in major cities involve creation of emission models and dispersion models as well as stochastic models for prediction of air pollution levels in future.

l The emission models inlcude ones like MOVES (MOtor Vehicles Emission Simulator) developed by the USEPA. The various dispersion models used in practice are developed considering Gaussian dispersion of continuous, buoyant air pollution plumes.

l These models, together with geographic data like road maps and meteorological data like windspeed can be combined to create thematic maps which show the spatial distribution of pollution in terms of specific pollutant concentration.

l If colour gradation is used, then the pollution levels could also be indicated and a borderline created in terms of health hazards. These type of 3D and 2D visualization of the problem can help the local authorities understand the gravity of the extent of pollution and take measures

Traffic Induced Air Pollution Control Work Flow Model

Conclusion

While these are all the scholarly uses of GIS, the availability of maps in our smartphones powered by GPS and GIS also make our dayto-day lives easier. We use it on a daily basis for the app-based cab service and public transport tracking systems and navigation systems. The synergy that GIS brings about in modern application based sciences is undeniable.

References

l Anitha SD Selvasofia,Prince G Arulraj,2016 : Accident and Traffic Analysis using GIS

l Aydinoglu,Senbil,Saglam,Demir,2015 : Planning of Parking Places on Transportation Infrastructure by Geographic Information Techniques

l Becky P.Y. Loo, 2006 : Validating Crash Locations for Quantitative Spatial Analysis: A GIS based Approach

l Derekenaris, Garofalakis, Makris, Prentzas, Sioutas, Tsakalidis, 2001 : Integrating GIS, GPS, GSM Technologies for the effective management of ambulances

l Exhibitions India Group, 2016 : Smart Transportation – Trasforming Indian Cities

l Ford , Barr, Dawson, James, 2015 : Transport Accessibility Analysis Using GIS : Assessing Sustainable Transport in London

l G. Wang, F.H.M. Van den Bosch, M Kuffer, 2008 : Modelling Urban Traffic Air Pollution Dispersion

l https://brilliant.org/wiki/dijkstras-short-path-finder/

l https://en.wikipedia.org/wiki/Kernel_density_estimation

l https://en.wikipedia.org/wiki/Dijkstra%27s_algorithm

l https://en.wikipedia.org/wiki/K-means_clustering

l Mohammed Aboussaedi, Rosmadi Fauzi, Rusnah Muhamad, 2016 : Geographic Information System (GIS) Modelling Approach to Determine the Fastest Delivery Routes

l Tessa K. Anderson, 2009 : Kernel Density Estimation and K-means Clustering to Profile Road Accident Hotspots

l Valley Metro, Presentation titled, ‘Transit Planning and Route Optimisation through GIS’

Thank You