Engineering System Group 6 Team leader: Arnav Kumar Agrawal Assistant Team Leader: Gurniamat Kaur
Introduction Aim : To gather, analyse and propose feasible solutions for traffic congestion problems in Hyderabad. Traffic Congestion: In brief when demand exceeds capacity. Demand, Capacity : Defined in terms PCU(Passenger Car Unit). PCU: A Passenger Car Equivalent is essentially the impact that a mode of transport has on traffic variables (such as headway, speed, density) compared to a single car. Private Car: 1 unit Motorcycle: 0.5 unit Bicycle: 0.2 unit Trucks/Buses: 3.5 units
Sub - Systems 1. Gathering of Case Studies Collects Raw Data in a tabulated manner. 2. Data Analysis Analyses Data for problems and identifies causes. 3. Propose Solution Proposes solutions and classifies them upon parameters. 4. Solution Analysis and Generalization Analyses the feasibility of solution based on different metrics and check their scalability.
Use Case Diagram
Work Done 1. Kukatpally 2. Mehdipatnam 3. Malkajgiri We have worked on case studies in above three areas. For presentation we will go through the work done in kukatpally Area.
Sub System 1 Data Collection 1. Purpose Gather statistics/data To enable analysis Suggest Techniques to automate data gathering. 2. Requirement Data collection team is asked to provide validated and reliable data that can be used for further analysis on traffic congestion problems related to Hyderabad city.
Procedure Data is collected through various techniques discussed further. Help is taken from the agency involved in the surveying for the Government which is Hyderabad Metropolitan Development Authority The data is classified on the basis of different parameters discussed in the next slide. The correlation between different categories is established. Simulation method for future has been proposed
Data Provided on Parameters Road Network Inventory Traffic Volume Count Pedestrian count Broad Lane used Parking Inventory Speed and Delay
Use Case Diagram selecting the appropriate area and collecting all the available data Providing the categorized data according to the demand of the next subgroup
Work Done in Kukatpally Case Study
Automation Techniques Camera based Radar based Overhead Toll Collection
Limitations Data is not up to date Real time data collection is not possible No manual data collection.
Sub System 2 Data Analysis Subsystem Aim: To home in on relevant data with the help of threshold parameters and analyse the same to identify problems and causes. Questions answered by the system(at least 2-3): What is congestion ? How did you arrive at the problem ? How do you find causes for the problem and classify them?
Procedure Initially, for each road whose data was given type of lane was identified. Using the data, the roads and junctions were classified into low, medium, high congestion. The roads and junctions with medium and high congestion were taken and the possible causes of congestion were analyzed using the relevant data and topological analysis was carried out using google maps. Other parameters which were situation specific ( population, bus stops, railway stations, etc) were also explored. Using these we tried to find their causes. The Causes were classified as Strong or Weak depending on the quantity of data to support our claim.
Measures Of Congestion 1) PCU/hr : PCU - It is a vehicle unit used for expressing highway capacity. Type Weight Car ,taxi,pick up 1.0 Cycle 0.2 Bus, truck,tractor 3.5 Motor cycle 0.5
Index of Saturation ( in PCU /hr) Ordinary two 750 Dual carriageway 3000 lane road(30 ft (60 ft width) width) Three lane road 1400 Motorway (3 6000 (central lanes each way) overtaking) 2) Average speed :-If traffic congestion is more , average speed will be low else it will be high . 3) Pedestrian count: It is the number of people walking past a location per unit time.
Threshold values 1) Average Speed Category Limit(kmph) Low Speed Zone 10 – 20 Medium Speed Zone 20-30 High Speed Zone > 30 2) PCU/hr Category Limit(PCU/hr) Low Congestion Zone < Index of saturation Medium Congestion Zone Index of Saturation – 2* Index of Saturation High Congestion Zone > 2 * Index of Saturation
3) Pedestrian Count Category Limit(Pedestrian / hr) Low Pedestrian Movement 0 – 100 Medium Pedestrian movement 100 - 300 High Pedestrian movement 300 - 500 Very High Pedestrian movement > 500
Use Case Diagram
Work Done in Kukatpally Case Study PROBLEM 1: TRAFFIC CONGESTION IN NH9 HIGHWAY – ROAD 4 EXIT Data used: Traffic counts of mid-blocks located along NH9 (2 lane road) like KPHB bus stop to JNTU and METRO to KPHB are 5683 PCU/hr and 5926 PCU/hr respectively, hence they fall under high congestion zone. Cause identified: Buses utilize road 1 to pick up passengers from KPHB phase 1 and 2 areas. They exit from road 4 into the NH9. The right turn bank over there causes traffic. This is a strong cause.
PROBLEM 2: TRAFFIC CONGESTION AT NH9 - JNTU JUNCTION Data used: Traffic Counts of mid-blocks located along NH9 (2 lane road) like KPHB bus stop to JNTU and METRO to KPHB are 5683 PCU/hr and 5926 PCU/hr respectively, which fall under high congestion zone. JNTU junction has wider roads (JNTU-KPHB is a 4 lane C/W road) connecting it but it has a very high traffic count of 7567 PCU/hr. The pedestrian count at the JNTU junction towards KHPB bus stop(2 lane) is 728ped/hr which is very high. One of the Causes identified: Short-route and long-route buses both travel from JNTU road which is already heavily congested and then takes a right turn to the highway 9 which causes congestion.
Future Scope and Limitations We have also made a excel of our case study which can be used for automation i.e. We can extract the relevant Google maps which can be helpful for better representation of our causes and also helpful for the next sub system for better representation of their solutions. The analysis depends solely on the quality and quantity of data provided. Sometimes based on threshold parameters for traffic count, pedestrian per hour, etc. we may ar rive at the problem but not its cause. The data needs to be updated for ensuring good analysis.
Sub System 3 Propose Solutions Aim To look at the problems along with their causes given by the previous sub- system. To come up with solutions and classify them based on whether they are short-term, long-term , policy-based etc. Input : Data analysis of previous system. Review from next sub-system. Output : As many solutions as possible classified whether they are - short-term, long-term, policy-based, infrastructure based or technology-based. What we set out to do We had tried to analyze the current solutions implemented and where were they lacking. However we could not find revelant data for it. Also we could not do a cost-based analysis.
Procedure 1. Read and understood the report submitted by the previous sub-system. Looked at the problems , their data behind it and the reasons for it. 2. Came up with as many solutions as possible without any restrictions. 3. Then, we eliminated some solutions if they were too infeasible in terms of cost, construction or technology. 4. Classified the solutions whether they are short-term , long-term, policy-based , infrastructure-based or technology-based. 5. Documented them with along with proper images of annoted maps to serve as examples.
Use Case Diagram
Work Done in Kukatpally Case Study Below are the solutions for only two of the problems presented for the case- study of Kukatpally. Problem 1 As mentioned in previous slides, local and long-range buses use the heavily congested JNTU road and take a right on highway number 9, causing congestion. Solution Proposed Buses on JNTU road come from Malaysian township circle. Rather than going straight, route the long-route buses on the 9th Phase road. 9th Phase road is equally wide and can support further load. Long-route buses would skip some bus-stops but would not be much of a hindrance. Analysis of solution covered by the next sub-system. Solution Type: Short-term, Policy-based
4/10/2014
Problem 2 Congestion caused by local buses taking right by exiting at Road number 4 on Highway 9 Solution Proposed Rather than taking a right at Road number 4, go ahead straight and come back later from the other side of the road. However this solution was scraped by the next sub-system for reasons which they will explain in a moment. Revised Solution Proposed Currently buses exit only from road number 4. This can be spread over all the other parallel roads. Traffic will get spilled over rather than being concentrated. Category: Short-term , policy-based 4/10/2014
4/10/2014
Limitations Could not visit the actual places. Had to rely on maps , internet and accounts of people living around there. Lack of availablity of proper data regarding actual solutions implemented. Knowledge of current solutions would had helped us in coming up with better solutions. Data available is slightly old and thus may not be reliable.
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