Thesis

Spatial Analysis of Traffic Congestions Visualisation and Assessment over Vadodara City

UTKARSH SHAH MG 2509 - M.Sc Geomatics and Space Application - CEPT University

1 Spatial Analysis of Traffic Congestions- Visualisation and Assessment over Vadodara city – Utkarsh Shah

ABSTRACT

Vadodara is industrial capital of Gujarat, and it is growing in pace with neighbour mega cities Ahmadabad, Surat, in every possible dimensions. The increasing level of urbanization indicates that more people are living in cities than before. The resulting economic growth has fuelled a jump in two wheelers and four wheelers, with increasing urban population. This pattern induces pressure on traffic flow and makes living in urban area difficult. This study was therefore carried out with the aim of applying GIS to investigate traffic congestions pattern in Vadodara city and determine the traffic behaviour on various junctions of the Central Business Districts of the city. Vadodara City is managed by Vadodara Mahanagar Seva Sadan, which has total of 13 administrative wards having an area of 149 Sq. Km. The traffic Survey was carried out on some selected 28 junctions across the city, a regular digital camera was used to count vehicular traffic. The data generated by traffic survey then modelled on road network for spatial analysis. The study aims to deliver the analysis results as the traffic pattern for Peak Non-peak hours, congestion hot spots, attractions for the congestions and management solutions that should apply for the sustainable urban traffic. The work recommends that there is a need for traffic control systems for Vadodara city and it also recommends that a GIS structure in addition to these traffic management techniques should be put in place to monitor traffic congestions in the city. Traffic data should be generated throughout the year and short-term surveys should also be carried out to predict Average Annual Daily Traffic at any given location. The Study also shows that GIS is a veritable tool that can be used to sustain an endurable flow of traffic in urban environment, provided it is built on a properly designed database, which must also be amenable to constant updating.

2 Spatial Analysis of Traffic Congestions- Visualisation and Assessment over Vadodara city – Utkarsh Shah

Table of Contents

1 Introduction 1.1 Introduction and Discussion.......................... 3 1.2 Statement of the Problems............................ 5 1.3 Study Objectives and Scope........................... 6 1.4 Thesis Outline....................................... 7

2 Urban Traffic Data

2.1 Study Area........................................... 8 2.2 Traffic Survey....................................... 10 2.3 Data Processing...................................... 12

3 Methodology

3.1 Spatial Analysis in GIS.............................. 15 3.2 Variations in Traffic Volume......................... 17 3.3 Congestion and Vehicular Emission.................... 18

4 Analysis of Traffic Patterns

4.1 Traffic Congestion hot-spots......................... 19 4.2 Peak Non-Peak Traffic Pattern........................ 24 4.3 Integration of Emission Factor....................... 26

Results............................................ 31

5 Conclusion and Recommendations.......................... 32

References.............................................. 33

3 Spatial Analysis of Traffic Congestions- Visualisation and Assessment over Vadodara city – Utkarsh Shah

1.1 Introduction and Discussion Traffic congestion occurs when a city’s road network is unable to accommodate the volume of traffic that uses it. This situation is caused by rapid growth in motorization and with less than corresponding improvement in the road network, traffic management techniques and related transport facilities. Thus, traffic congestion is a phenomenon that is associated with urban environment all over the world. This is because we need transport to move from one place to another, especially when trekking becomes inefficient. While traffic congestion has been managed very well in some developed countries, it has continued to defy solutions in the developing world. The forecast of Global Traffic Volume (GTV) shows that the phenomenon would double between 1990 and year 2020 and again by 2050 (Engwitch, 1992). This type of growth pattern, as envisaged by the end of year 2020 and 2050, is an indication of what the future congestions portends for people living in urban environment. Private vehicles account for 30% of the total transport demand in urban areas of India. An average of 963 new private vehicles is registered every day in Delhi alone. The number of automobiles produced in India rose from 63 lakh (6.3 million) in 2002-03 to 1.1 crore (11.2 million) in 2008-09. However, India still has a very low rate of car ownership. When comparing car ownership between BRIC developing countries, it is on a par with China, and exceeded by Brazil and Russia. Motorised two-wheelers like scooters, small capacity motorcycles and mopeds are very popular a mode of transport due to their fuel efficiency and ease of use in congested traffic. The number of two-wheelers sold is several times that of cars. There were 4.75 crore (47.5 million) powered two wheelers in India in 2003 compared with just 86 lakh (8.6 million) cars. Such a sharp growth has many downsides the list of downside include traffic congestion. Not only traffic congestion but in a global scenario the aspect of environmental price we pay must be taken care of. The number of vehicles in India is equal to the number of vehicles in the U.K (Developed Nation). It is surprising fact however that road causality in India in 2007 is 1.13 lakhs while the same statistic for U.K is under 3000. Public transport is the predominant mode of motorised local travel in cities. Intermediate public transport modes like tempos and cycle rickshaws assume importance in medium size cities.[9] However, the share of buses is negligible in most Indian cities as compared to personalized vehicles, and two-wheelers and cars account for more than 80 percent of the vehicle population in most large cities.

Traffic in Indian cities generally moves slowly, where traffic jams and accidents are very common. India has very poor records on road safely—around 90,000 people die from road accidents every year. At least 13 people die every hour in road accidents in the country; also in the year 2007 road accidents claimed more than 130,000 lives, overtaking China.

Gujarat is one of the most developed and fastest growing markets of India, and one of the most industrialised states of the nation. Vadodara is the third most populated city of the state and is one of four cities in the states with the population over 1 million. Vadodara is considered to be one of the fastest growing markets for automobile sales. The vehicular population is increasing at alarming rate of 8-9 % per year. The rate of growth is likely to be higher in the coming years with vehicle prices dropping, affordable financing schemes increasing and disposable income being on the rise.

4 Spatial Analysis of Traffic Congestions- Visualisation and Assessment over Vadodara city – Utkarsh Shah

Further, the increasing influx of floating vehicles and transit vehicles would worsen the traffic situation by 2020.

More than 50% of the existing vehicular population comprise two-wheelers. However, it is well documented that more and more people prefer four-wheelers due to easy availability of vehicle finance and longer equate monthly instalments (EMI) terms. Even if there is a 5% shift in vehicle holding pattern, road space would further increase considerably. If new roads are not laid and the existing roads are not widened with proper provision for pedestrian traffic movement within the next two to three years, it is expected that the average speed of vehicular traffic would drop to less than 8 Km/Hr. In other words, the time taken during peak hours to travel from e.g. Makarpura to Sayajigunj will be more than an hour. Most of the vehicular traffic in Indian cities is disoriented and Vadodara is no exception. The disorientation brings with it some headaches of modern day life in the form of traffic blockages, low average traffic speed, collisions and other issues. Though till there is no monitoring system that can be used for traffic control and management. This grim scenario can be averted by an integrated traffic control system with GIS interoperability can help to monitor the traffic congestion and also can suggest solutions in real time. Intelligent traffic control systems and GIS is a valuable attempt to improve current situation.

5 Spatial Analysis of Traffic Congestions- Visualisation and Assessment over Vadodara city – Utkarsh Shah

1.2 Statement of the Problems The automobiles have an inevitable appetite for space. It uses space at home, at work, shopping places, religious centres and recreational centres. Ironically, when some of these spaces are empty, they are still reserved for the automobiles. Thus, a large chunk of the urban land, which could have been used up for productive activities, is consumed by the transport sector. The roadway carrying capacity, also, determines the maximum number of vehicles that would pass through a given section of a lane or road way in one direction or both for a two lane roadway, during a given time period. Thus, as traffic volume increases, the speed of each vehicle is influenced, to a large measure, by the speed of the slower vehicles. Thus, as traffic density increases, a point is reached where all vehicles would travel at the speed of the slower vehicle. This condition, when attained, indicates that the ultimate capacity has been reached and that would result in congestion on the road. In a highly urbanized environment, the automobile is a significant contributor to environmental pollution. The extent of pollution depends on certain variables such as the age of the vehicle, the type of the vehicles and the quality of the fuel used. Some of the effects of pollutants include respiratory diseases, caused by oxides of Nitrogen, and aggravation of asthma on those who are already afflicted with this disease. Apart from this, carbon monoxide, which is a dangerous pollutant from automobile exhaust, affects blood and prevents easy flow of oxygen from getting to the brain, the heart and other tissues. Urban areas have been noted to be very busy with automobiles, especially during the peak periods. During such peak periods, traffic noise comes from vehicle engines, exhaust systems and horns. Busy urban roads generate between 70-85 decibels of noise, depending on the characteristics of the traffic, speed and type of road surface. The tolerance level of noise is put at 66-68 decibels; meaning that with 70-85 decibels, a significant number of people are irritated and the negative effect of noise on health could be better imagined (Ameyan, 1996). The areas close to airports witness a lot of noise Pollution, especially where development is fast, as it happens in most of our urban centres today. Illegal parking is also a major problem in urban environment. This is because parking on roadside, which is a common phenomenon, reduces the traffic corridors meant for the efficient movement of automobiles. Thus, it becomes a major problem in cities and especially in the Central Business District (CBD), where multi-storey buildings are common and the land use is devoted mostly to commercial purposes. The resultant effect of such illegal parking, therefore, is traffic congestion. This illegal parking leads to delay in travelling time and increases the cost of travelling because more fuel is used up in the process of accomplishing a delayed journey (go-slow / traffic jam).

Damaged Roads & Crazy Parking Leave No Room for Pedestrians (Junction of R.C. Dutt Road & Productivity Road)

6 Spatial Analysis of Traffic Congestions- Visualisation and Assessment over Vadodara city – Utkarsh Shah

1.3 Study Objectives The main aim of this study is to obtain more insight into urban traffic by analysing variations in traffic volumes for selected junctions by using spatial techniques of GIS. Evaluation of congestion levels by studying the travel need and traffic attractions over CBD’s of the study area. Landuse change affects transportation activities greatly. Analyse the patterns for Vadodara city, resulting in insight into urban traffic pattern. It is investigated what typical urban travel patterns can be distinguished, what temporal, circumstantial and spatial factors are on the basis of these patterns and how these patterns can be explained for by variations in travel demand and/or supply. Scope and Limitations The analysis is based on traffic volume generated by manual traffic survey over the study area within 7 days survey due to limited amount of time and resources, which doesn’t incorporate past traffic data due to unavailability of traffic count loops or control systems installed in the city, so the patterns identified are purely based on traffic volume count on each junction. Since travel time data is in general not available for the urban network, the reliability of travel times is not investigated in this thesis. The obtained insight into variations in traffic volumes could however be applied for the analysis of travel time reliability. The study described in this thesis focuses on within and between day variations in traffic volumes. Short term variations due to traffic light cycles and short term disturbances like the loading of a truck or a bus stop are not analysed. Moreover, since only one week of traffic data is studied, long term variations due to changing land use patterns or infrastructural changes are not taken into account. Variations in public transport use and the number of bicycle trips are not part of this study, although variations in these factors may (partly) explain for variations in the amount of motorized traffic. In this thesis, the term motorized traffic refers to all traffic that uses the main road and is observed by the digital camera, i.e. cars, trucks, buses, motorbikes, mopeds. Furthermore, no distinction is made between different types of motorized traffic. (Fuel Category, Engine Type etc.)

7 Spatial Analysis of Traffic Congestions- Visualisation and Assessment over Vadodara city – Utkarsh Shah

1.4 Thesis Outline Figure 1 presents an overview of the structure of this thesis.

Figure 1: Schematic overview of the structure of the thesis.

Chapter 1

Introduction, Study Need, Objectives and Scopes.

Chapter 3

Analysis Methods in GIS, Variations In traffic Volume, Evaluation Congestion levels, and Design Framework for Analysis

Chapter 2

Study area, Traffic congestion factors, Survey, GIS data Processing

Chapter 5

Conclusion, recommendations

Chapter 4

Studying Traffic Pattern, Congestion Hot-Spot, Visualization and Evaluation of traffic conditions with emission load over Vadodara city in GIS

8 Spatial Analysis of Traffic Congestions- Visualisation and Assessment over Vadodara city – Utkarsh Shah

2 Urban Traffic Data 2.1 Study Area Vadodara is located at 22.30°N 73.19°E in western India at an elevation of 39 metres (123 feet). It is the 18th largest city in India with an area of 148.95 km² and a population of 1.6 million according to the 2001 census. The city sits on the banks of the River Vishwamitri, in central Gujarat. Vadodara is divided by the Vishwamitri into two physically distinct eastern and western regions. The eastern bank of the river houses the old city, which includes the old fortified city of Vadodara. This part of Vadodara is characterised by packed bazaars, the clustered and barricaded Pol system of shanty buildings, and numerous places of worship. It houses the General Post Office and landmark buildings like Laxmi Vilas Palace, Mandvi area and Nyay Mandir. The colonial period saw the expansion of the city to the western side of Vishwamitri. This part of the city houses educational institutions like the Maharaja Sayajirao University, the Vadodara Railway Station, modern buildings, well-planned residential areas, shopping malls, multiplexes and new business districts centred around R. C. Dutt Road, Alkapuri, Nawayard and more recently, the Old Padra Road and Gotri.

Vadodara is administered by the Vadodara Mahanagar Seva Sadan(VMSS) . Some of the regions surrounding the city are administered by the Vadodara Urban Development Authority (VUDA). The two main institutions involved in planning and development in Vadodara are the Vadodara Mahanagar Seva Sadan and the Vadodara Urban Development Authority. The jurisdiction of both these agencies is demarcated clearly not only physically but also functionally. The governing acts for both the institutions differ. The principal responsibility of VUDA is to ensure a holistic development of the Vadodara agglomeration covering an area of 714.56 km² Whereas VMC is involved in the development of a limited area of 149 km². Figure 2 Showing Land-use map of Vadodara city which is the focus of my study area.

Population of Vadodara city is growing at an Avg. annual rate of 5.04 %

0200000400000600000800000

100000012000001400000160000018000002000000

1961 1971 1981 1991 2001 2010

Popu

latio

n

Year

Vadodara City Population Trend

Population

9 Spatial Analysis of Traffic Congestions- Visualisation and Assessment over Vadodara city – Utkarsh Shah

Figure 2. 2006 Land Use MAP of Vadodara City

Base Map of Study Area

10 Spatial Analysis of Traffic Congestions- Visualisation and Assessment over Vadodara city – Utkarsh Shah

2.2 Traffic Survey Traffic Survey was carried out for total of 28 Junctions over Vadodara city. The Survey Method used is conventional manual counting. Traffic Count was carried by Manual observation with using regular digital camera to record one cycle to cross check the manual observation and finalise actual count. Digital Camera was very helpful for splitting the type of vehicles form total count. Because there are no such automatic vehicle counting systems installed in Vadodara manual traffic counting takes much of the time and resources and less time for traffic data analysis. Thus Short-term traffic counts were carried out to generate traffic data on selected road segments. Permanent automatic traffic recording stations provide continuous counting of the traffic on selected roads (mostly on highways) for the entire year. The advantage is to offer traffic counts that are typically recorded in 15 minute or hourly intervals, 7 days a week and 365 days a year intervals. It thus enables a finer level of analysis and a more accurate annual average than short-term counts. Permanent automatic traffic recorder is the only way to provide exact Average Annual Daily Traffic values (when used under perfect conditions). Short-term traffic counts (also called seasonal, portable or coverage counts) provide roadway segment-specific traffic count information on a cyclical basis for a large number of road segments. The data were collected from 1 to 7 days in 15 min intervals. Due to differences in day-to-day variation in the traffic flow, short-term traffic counts needs to be assessed with the AADT, to estimate the actual flow of any junction. Each junction was surveyed for morning Peak hours non-Peak hours and evening peak hours and for weekends. Timings for morning peak, non-peak and evening peak were from 9:00 to 11:00 a.m., 13:00 to 15:00 p.m. and 18:00 to 20:00 p.m. Figure 3.0 showing the map of selected junctions for traffic count survey of Vadodara city.

Vadodara is considered to be one of the fastest growing markets for automobile sales. The vehicular population is increasing at alarming rate of 8-9 % per year. The rate of growth is likely to be higher in the coming years with vehicle prices dropping, affordable financing schemes increasing and disposable income being on the rise.

Growing at rate of 8-9 % per year

11 Spatial Analysis of Traffic Congestions- Visualisation and Assessment over Vadodara city – Utkarsh Shah

Figure 3.0 Map of Selected Junction for Traffic Counts Survey.

12 Spatial Analysis of Traffic Congestions- Visualisation and Assessment over Vadodara city – Utkarsh Shah

2.3 Data Processing Road Network of Vadodara city is taken from Vadodara Urban Development Authority (VUDA) as CAD drawing and transformed to ESRI shape-file for network analysis. Vadodara Municipal Corporation limit was also taken from VMC with division of VMC in 13 Administrative wards. Demographic profile of Vadodara city is derived from Census of Gujarat 2001. Traffic count surveys were performed using the Garmin 72 GPS to store the junction location and Nikon L 20 was used to take video coverage of traffic to generate the types of vehicles during each cycle, and driving speed of the vehicle. Traffic Count Sheets was prepared to write the counts of each junction. There are total of appx. 36000 road segments which in total constitute 700 km of road network of whole Vadodara city. Data sets then converted to geometric road network in ArcInfo 9.3 to perform network analysis and spatial analysis. Due to data errors in CAD drawing there are some geometry error with the network but it doesn’t affect the analysis because the main aim is to perform the various spatial analysis to understand traffic behaviour. There are many transportation and planning software packages available in the market for micro traffic analysis and planning. For e.g. TransCAD, EMME, etc. These software and tools can help to generate better visualization scenario and helpful to evaluate the present condition of city but it is difficult to use when project is carried out on individual basis without any financial support. Hence whole analysis and data compilation was done in one GIS platform Arcinfo 9.3. Integration of Various planning tools can help built more strong analysis system for traffic studies and planning in future. Table 1 showing the traffic data collected for all 28 junctions. Data was collected for 15 min four cycles for 2 hours. I.e. total 8 cycles of 15 min interval for 2 hours survey, for each time during morning, non-peak and evening peak for a weekdays and weekends. Modal split into 3 main category of vehicles MC- Motor Cycle, AR/3-W- Auto rickshaws and other 3 wheelers, and Car – personal and private motor cars of all categories. (Petrol and diesel). These are point data and that has to be transformed into line segments of the road. Junction split was also carried out with the help of video footage and total vehicle counts split into 4 arms of junction to visualize traffic trend of a particular junction. Traffic Volume – Vehicle Kilometres Travelled refer to the distance travelled by vehicles on roads. It is often defined as an indicator of traffic pressure (or traffic demand) and is generally used to indicate mobility patterns and travel trends. For one considered link, the vehicle-kilometre is calculated by multiplying the AADT by the length of the link (in km). VKT for a motorway area can then be obtained by adding up the VKT of each segment. As a basic example of estimation, the AADT on a motorway segment can be given by:

Where, TFs, 24j is the 24-hour traffic flow on segment s at day j. In this case, the average daily traffic

volume can be estimated as:

Where, Ls is the length of the segment s and Ns the total number of segments.

13 Spatial Analysis of Traffic Congestions- Visualisation and Assessment over Vadodara city – Utkarsh Shah

Table 1 Traffic Data Extrapolated (FROM TRAFFIC SURVEY-Traffic Flow Vehicle per hour)

J_Id J_Name Total

Morning Peak(9:30 to 10:30)

P/H

Total Non Peak(14:00

to 15:00) P/H

Total Evening

Peak (6:30 to

7:30) P/H

Total Weekend(Saturday

And Sunday) P/H

MC AR/3-W

CAR Average Total

vehicle P/H (one

week Avereage)

1 Susan 8724 4000 7838 5686 4097 1560 1197 6854

2 Harinagar 4587 2974 6156 7283 2567 567 1124 4258

3 Ambedkar Circle

7720 3921 9288 9089 3840 856 2307 7003

4 Productivity 3way

7048 5798 8956 8704 4170 1327 1223 6720

5 Kalpatru 8020 3675 8971 9217 4029 1152 1413 6594

6 Genda Circle

8152 4868 10764 9675 4216 760 1636 6612

7 Fatehgunj 10976 5687 14432 10739 6149 1953 2461 10563

8 mandvi-BOB

7666 5720 10241 9759 3973 1628 492 6093

9 Mandvi-Gate

8764 4704 9889 8910 4325 1432 396 6153

10 Saffron 9849 5692 8452 11023 5396 1036 2040 8472

11 KalaGhoda 12034 5698 11488 9882 6154 1769 1372 9295

12 Kothi 6514 2988 7859 8548 2575 1460 476 4511

13 Nyay-Mandir to

gandhigruh

9724 6456 8726 10783 3051 1256 587 4894

14 GandhiGruh 7812 5983 9540 9115 3567 1174 495 5236

15 Akshar Chowk

6442 2879 8642 7280 3488 683 986 5157

16 Munj-mahoda

6622 3765 7855 6745 3577 936 947 5460

17 Manjalpur fatak

7102 3967 8735 7839 3028 845 1180 5053

18 Saraswati Complex

5694 3116 6854 4589 3804 497 926 5227

19 Manmohan 4784 2968 5438 6574 2451 989 676 4116

20 Tower Crossing

6235 4672 5128 4679 2872 1626 489 4987

21 Chokhandi 4068 2563 5873 6651 2707 778 228 3713

22 Vuda Circle 10360 6233 11378 10800 4916 1053 2498 8467

23 Sangam Crossing

8915 5723 9018 9942 3421 1121 891 5433

24 Airport 8921 3429 7348 6790 3213 892 758 4863

25 Amit Nagar 9970 3657 10076 8219 3821 749 1011 5581

26 Channi 6289 2438 7218 7431 2315 672 934 3921

27 Panigate 7821 3428 6728 5721 2937 874 532 4343

28 Gorwa 8291 3215 7931 8561 3891 1011 881 5783

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Number of Avg. Motor Cycle per Hour

Number of Avg.3 Wheelers per Hour

Number of Avg. Motor Car per Hour

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3.0 Methodology 3.1 Spatial Analysis To utilize spatial analysis methods and analyze road network traffic state, it is necessary to establish a spatial model to describe road network traffic data from spatial perspective. Following definitions are given to describe some of the modelling assumptions. Firstly, since urban traffic network is composed of many road links and intersections in terms of spatial structure, it can be abstracted as line pattern objects and point pattern objects. Definition1: A road intersection is treated as a node which has more than two adjacent links. Definition2: A road link is a segment between two consecutive intersections. Since it is possible to define a time interval Δt in which traffic state of a road link is relatively homogeneous, a road ink can be abstracted as a point pattern defined by its traffic state Definition3: A road link can be abstracted into a Link Point which is the mid-point of the road link, and the attribute of Link Point is the traffic state during time interval Δt. Therefore, a road network is transformed into sparse points in space, and traffic data upon it (St) is split into cross-sectional data pn by Δt (see Fig.1).

Distance is the most important indictor to reflect the extent of spatial correlation between spatial objects. Because intersection is the major issue affecting urban traffic state, our model uses the number intersections between two road links as the distance of them in network space instead of their real distance in free space. Definition4: The network distance between two road links is the least accessible number of intersections between two link points.

SPATIAL ANALYSIS TECHNIQUES

1. Spatial autocorrelation analysis. 2. Spatial dependency analysis.

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Traffic data is usually not randomly distributed over road network but takes on association and aggregation, therefore, traffic state spatial dependency and heterogeneity are two important aspects in understanding road network performance. The First Law of Geography [6] point out that, adjacent geographical units exists spatial dependency, and it decays with increase of distance. Goodchild et al. [7] also pointed out spatial data tends to local aggregated and also shows unstationary, therefore, spatial heterogeneity coexist with spatial dependency. In this section, we introduce two spatial analysis techniques to be used in this study to analyze traffic spatial dependency and spatial heterogeneity

17 Spatial Analysis of Traffic Congestions- Visualisation and Assessment over Vadodara city – Utkarsh Shah

3.2 Variations in Traffic Volume Urban traffic clearly is not a static phenomenon. Traffic data collected during a week of period vary in both time and space. But due very small sample, the variation in traffic volume viz. temporal variation and spatial variation analysis will not give desire traffic volume pattern over given space and time. The topic provides existing literature in urban situation for assessing temporal and spatial variation in traffic volume. 3.2.1 Temporal Variations Temporal variations in traffic volumes can be analysed at different time scales, ranging from minute-to-minute variations to year-to-year variations.

Common time scales for analysing temporal variations in traffic volumes.

3.2.2 Spatial Variations Analysing spatial variations in urban traffic volumes sec (without taking the temporal component into account) results in the identification of locations with high traffic volumes for a certain time interval. The results of these analyses are location specific and do not provide more insight in urban traffic in general. It is more interesting to analyse spatial variations in combination with temporal variations. As with temporal variations, analyses can be executed at multiple time scales. Moreover, deferent spatial aggregation levels can be distinguished. Figure shows analyses that can be executed on deferent combinations of temporal and spatial aggregation levels.

Figure shows Variations in traffic volumes at different spatial – temporal aggregation levels.

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3.2.3 Congestion and Vehicular Emission These three factors have a strong relation. Any change of one factor among the three might lead to the change of the performance of the whole system. Change in land use patterns might change transport resistance between certain locations and might reduce travel needs also; good planned activity locations might contribute greatly to relief of traffic congestion, reducing travel time/distance as well as improving environment (Wee and Maat 2003).

Relationship between activity locations, travel need and transport resistance

(Adopted from (Wee and Maat 2003)) In total, 28 road segments which are suffering severe traffic congestion in the current situation and/or have congestion potential in different scenarios have been selected for evaluation. Based on the comparison between traffic count data on same peak hours, road intensity, and local knowledge, and Emission Factor of congestion levels were proposed for evaluation of congestion in the current situations and scenarios. Air Quality of urban areas are affected by a variety of complex source mix, this study investigates the contribution of vehicular emission derived from Emission Factor Inventory developed by ARAI, for surveyed junctions and to compare over all air quality by all sources. Emission Factors for different vehicle category and types were used to calculate emission rates of surveyed traffic junction and representation of emission rates by interpolation method on particular junction with assumption that VKT is 1 km for all vehicles around that junction. Because Emission Factor is calculated in gram/km and so VKT profile for each junction and major road links will need detail amount of survey on travel trips from each locations, road data with class attribute, and this will need much more time so emission rates are represented as point source on survey points instead of line source on road segments. 1km buffer generated around survey points to show emission dispersion from that junction where vehicle count survey was performed.

location of activities

needs, desires

passenger mobility volumes

travel resistance

19 Spatial Analysis of Traffic Congestions- Visualisation and Assessment over Vadodara city – Utkarsh Shah

4.0 Analysis of Traffic Pattern

4.1 Traffic Congestion Hot-Spots

Congestion by Types of Vehicles

Map.1 Motor Cycles Map.2Cars

Map.3 3 Wheelers

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Congestion Pattern at Different Peak Hours

Map.4 Morning Peak Map.5 Non-Peak

Map.6 Evening Peak

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4. Analysis of Traffic Pattern 4.1 Traffic Congestion Hot-Spots

Map.7Evening Peak Hot Spots Map.8Morning Peak Hot Spots

Map.9Non Peak Hot Spots

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Map.10Motor Cycle Hot Spots Map.113 Wheelers Hot-Spots

Map.12Car Hot Spots

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Map.13Average vehicle per hour per day

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4.2 Peak Non-Peak Traffic Pattern

Spatial Autocorrelation

Moran’s I Index = for Non Peak Moran’s I Index = for Morning

Moran’s I Index for = Evening Moran’s I Index for = Weekend Spatial autocorrelation analysis used to measure spatial dependency, which assesses the extent to which the value of a variable at a given location influences values of that variable at contiguous locations. The Moran I value is usually between -1 and 1, and it is statistically significant and positive when the observed value of locations within a certain distance (d) tend to be similar, negative when they tend to be dissimilar, and approximately zero when the observed values are arranged randomly and independently over space. The extent of spatial autocorrelation decreases with increase of road junction distance, which coincides with real-world situation that traffic behaviour, is usually dissimilar between two far apart junctions. In this case, it shows that Moran I trends to 0 (<0.1) from 5 order in morning peak, which

25 Spatial Analysis of Traffic Congestions- Visualisation and Assessment over Vadodara city – Utkarsh Shah

means that the average influence degree of one junction is four road links in road network; and morning’s Moran I is bigger than other periods, which means that influence degree of junction in morning is stronger than other periods, in other words, the distance between two junction link have similar traffic behaviour is longer than other periods.

Congestion Level with Land Use

Map.14

The movement of people takes place in space and time, from one land-use location to another at a certain time of the day. Most trips are made because people want or have to carry out activities. Land-use is spatial distribution of activities. This distribution generates traffic for people move from one activity to another. Travel demand depends on the utility of the activity, on the one hand, and on the aggregate costs to reach the destination, on the other hand. The aggregate costs measured in time, money, and efforts needed to cover the distance are usually expressed as transport resistance or impedance. Eventually residents tend to move to more accessible locations. Therefore, one of the fundamental axioms of transport planning is that people do not like travelling, and they only travel because the benefit received at the destination (work, education) more than outweigh the cost (i.e. time and money) of getting there (Blijie and Bok 2002; Wee and Maat 2003; Banister 2002). Here we can see that high traffic volumes are on the junctions around the industrial and commercial zone of the city during an average day.

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4.3 Integration of Emission Factor with Vehicle Data.

The Automotive Research Association of India (ARAI) was responsible for developing EF for vehicular emissions, based on mass emission tests conducted on limited number of in-use vehicles covering different engine technologies, types of vehicles, vintage, types of fuels, etc.

Source: ARAI

Source: ARAI

27 Spatial Analysis of Traffic Congestions- Visualisation and Assessment over Vadodara city – Utkarsh Shah

Emission Load (gram/per hour) Maps-Average Daily Traffic Flow per hour

Map.15 Map.16

Map.17

28 Spatial Analysis of Traffic Congestions- Visualisation and Assessment over Vadodara city – Utkarsh Shah

Map.18 Map.19

Map.20

29 Spatial Analysis of Traffic Congestions- Visualisation and Assessment over Vadodara city – Utkarsh Shah

Map.21 Map.22

Map.23

30 Spatial Analysis of Traffic Congestions- Visualisation and Assessment over Vadodara city – Utkarsh Shah

Emission Factor developed by ARAI was used with traffic data to generate PM10, CO, NO2 emission loads. Emission load is depending upon Vehicle Kilometre travelled, and that is evaluated with average travel trips generated for particular type of vehicles. Public survey was very helpful to figure out average daily travel trips generated, fuel used, vehicle type for different areas. Maps generated above, are assumptions that every vehicle at least travelled 1km around the junction, and so emission loads by vehicles on that junction is calculated with multiplying EF (g/km) to no. of average vehicles per hour per day passes through that reference point. For 4- wheelers motor car it is assumed that 60 % of vehicle population uses petrol and 40 % uses diesel. Emission loads at different junction interpolated with Spatial Analyst tool, in ArcGIS 9.3, and maps were made for different vehicle types and its emission loads on particular junctions. Average wind speed and direction was assumed to pollution dispersion, that is for 3-5 m/s and there are two main wind directions for Vadodara city, eastward and westward. Emission load maps will be helpful to correlate air quality by vehicular emission, and other non-vehicular emission, to predict and monitor ambient air quality, and that will be cross checked by regular air sampling. Comparison of Emission rates from vehicle exhaust derived from vehicle emission factor with the annual average emission from all sources is represented here to show the contribution of vehicular emission on air pollution.

Here, we can see that in the year 2009 NO2 annual average concentration from all sources was 28 microgram/m3 on the west zone of the city, where short term traffic count showed that higher frequency of 3 wheelers in west zone contribute NO2 emission rate of 683 gram/ hour on an average day, and Motorcycle and Car contribute to north and north east zone of the city where emission rates ranging from 100 to 1100 gram/hour on an average day.

31 Spatial Analysis of Traffic Congestions- Visualisation and Assessment over Vadodara city – Utkarsh Shah

Results

Traffic flow varies with the location and time of the junctions and land use characteristics, derived from spatio-temporal analysis.

Congestion level is dependent upon the number of particular vehicle type on that junction, vehicle speed and signal timings.

Fatehgunj, Kalaghoda and Vuda Circle are the three main junctions at which traffic flow is high during morning and evening peak.

Motor-cycles traffic is high at almost every junction; due to higher population of motorcycles in the cities.Car traffic is high on the main roads and outer area of the cities, away from the core business districts.

Fatehgunj circle records the highest traffic flow of day average of 10563 vehicles per hour. Chokhandi records the lowest traffic flow of day average of 3713 vehicles per hour.

During morning peak hours traffic flow is higher on the junctions located in the residential, and semi commercial zones where educational institutes, hospitals, and other markets oriented travel trips are recorded. Higher traffic flow recorded during evening peak hours where junctions located nearby Shopping Complexes, hotels and restaurants, cultural campuses.

Hot Spot analysis of traffic pattern describes hot and cold zones of traffic on the basis of types of vehicles and day time.

Genda circle has highest z score during an evening peak hour, and Vuda circle has highest z score during morning peak hour contributing to the hot junctions of traffic congestion.

Genda circle, Fatehgunj circle, Saffron circle, Productivity road, accounts hot junctions of traffic congestion based on average traffic flow per hour per day.

Motor cycle contributes as major source of vehicular emission in all areas ranging from 400 to 1095 gram/hour, 3 wheelers ranging from 174 to 683 gram/hour and motor car contributes 100 to 1000 gram/hour depending upon vehicle frequency on the surveyed junctions on an average day.

Vehicular emission factors used in this study to interpolate emission rate of a junction can be used to compare emissions from all other sources.

32 Spatial Analysis of Traffic Congestions- Visualisation and Assessment over Vadodara city – Utkarsh Shah

5.0 Conclusions and Recommendations

On one hand, the distribution of urban traffic behaviour is unstable; on the other hand, urban traffic behaviour shows the feature of local aggregation, which is influenced by both road link location and road network structure. All the spatial characteristics of traffic behaviour exhibit certain temporal features.

This study was carried out with the traffic data generated by manual traffic survey on individual basis without any financial support, more analysis and research is needed to come to a conclusion, where detailed traffic study and temporal data needed, to state the traffic behaviour and patterns for given study area.

The used retrospective modelling approach could be quite easily applied when traffic data but no air pollution monitoring data (or sparse monitoring network) is avail-able. It helps to see also the trends in traffic density and air pollution over longer periods. Nowadays the traffic models can provide reliable traffic flows even with very few measurements sites. The application of air pollution modelling can give a broader picture of environmental health concerns related to urban traffic.

33 Spatial Analysis of Traffic Congestions- Visualisation and Assessment over Vadodara city – Utkarsh Shah

References and Literature Review Vadodara Urban Development Authority Vadodara Municipal Corporation Weijermars, W.A.M., Analysis of Urban Traffic Patterns Using Clustering, T2007/3, April 2007, TRAIL Thesis Series, the Netherlands Haixiang Zou, Yang Yue, Qingquan Li and Yonghui Shi, A Spatial Analysis Approach for Describing Spatial Pattern of Urban Traffic State, 2010 13th International IEEE Annual Conference on Intelligent Transportation Systems Madeira Island, Portugal, September 19-22, 2010 Dr. E. F. Ogunbodede, assessment of traffic congestions in akure (nigeria) using gis approach: lessons and challenges for urban sustenance. Nguyen Ngoc Quang , Mark Zuidgeest , Mark Brussel , Development of an integrated GIS-based land use and transport model for studying land-use relocation in Hanoi, Vietnam Ben Alexander Wuest and Darka Mioc, Visualization and modeling of traffic congestion in urban Environments Crisil Infrastructure advisory, Infrastructure report on Vadodara Municipal Corporation www.en/wikipedia.org