Development of Behavioural Models of Travel for Metropolitan Areas Padmini G and S.L Dhingra (IIT Bombay)

DEVELOPMENT OF BEHAVIOURAL MODELS OF TRAVEL FOR METROPOLITAN AREAS

Padmini G, IIT Bombay, [email protected]

Prof.S.L. Dhingra, IIT Bombay, [email protected]

ABSTRACT

Urban population in is growing rapidly from the last two decades; from this metropolitan cities are worst hit due to fast trends of urbanization. Because of urbanization the crisis pertaining to metropolitan areas is role of transport. To overcome transportation problems proper mass transportation facilities are required, to provide new transport facilities study of commuters behaviour is an important aspect. More number of attempts has been made by the researchers to develop integrated urban activity and mass transport models for metropolitan areas. But these were not explored fully for metropolitan areas. For study region , (India) population density increased clustering and land use mix, infill development of vehicle traffic can reduce public costs. At the same time, income growth encourages car ownership, followed by a modal shift from public transport to road oriented transport mode. Discrete choice models for vehicle ownership models and mode choice models are developed for Pune metropolitan area. Car and Two wheeler (TW) ownership models are developed using Revealed preference (RP) data obtained from HIS data of Pune. Data base is prepared for Mode choice using skims extracted from the CUBE for Pune network and based on Home Interview Survey (HIS) data. Mode choice model is developed using that data set. Types of alternative modes considered in the preparation of mode choice model are walk, car, TW and Public transport (PT). Though all the models have logical and statistical significance prediction success tables are written to find out the goodness of fit of the models developed. From the prediction success tables of the models it was observed that goodness of fit of the model is satisfactory. The major part of this study is the analysis of HIS data and development of various models like car, TW ownership and Mode choice Models. It was concluded that disaggregate modelling approach can be successfully used for modelling the Vehicle ownership decisions, and, mode choice for better transportation planning of the cities in developing countries.

Keywords: Revealed preference (RP), Stated Preference (SP), Vehicle ownership Models, mode choice models, prediction success table.

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1.0 INTRODUCTION

Urban population in India is growing rapidly from the last two decades, from this metropolitan cities are worst hit due to fast trends of urbanization. The urbanization has brought in its own problems, especially with regard to impact on the infrastructure facilities. The urban transport system has come under heavy strain and has adversely affected the quality of life of urban dwellers. Due to lack of public transport and rapid urbanization the present transportation facilities are not enough to cater the needs of people, because of this reason vehicle ownership increases rapidly from last two decades. One of the crisis pertaining to urban traffic is the role of public transport. Mass transport facilities in the cities provided by buses and suburban trains are grossly inadequate for providing fast, comfortable, and convenient travel. Crowded buses, large queues at bus stands and excessive bus travel time due to added street congestion are today common features of all big cities. This has resulted in heavy shift of commuter patronage from mass transportation to private and intermediate transport leading to an imbalance in the modal spilt and consequently, a huge increase in number of intermediate and private vehicle ownership. It is true that the private car is the most ideal transport vehicle devised by man. Its advantages are quicker travel, freedom from adherence to fix timetables, possibility of carrying the family and luggage more comfortably along with the sheer enjoyment of driving. But, at the same time, the great increase in car ownership has resulted in inefficient road space utilization and has created problems of congestion, accidents, parking, and pollution, also the concept of door-to-door service provided by car is lost. To improve our city streets only for the sake of growing needs of some future car travelers might not be feasible with the limited resources available. More Vehicle ownership causes congestion and more fuel usage. The behavioural models are useful for arriving realistic decision frame work, and the various choices involved in location and travel aspects in mega cities of developing countries.

The covers an area of about 443 square kilometers. It comprises of two municipal corporations and three cantonment boards. The city is well connected by Road, Air and Railway routes. Pune is the second largest city in . It has a broad, multi functional economic base comprising cultural, educational, business, trade, commerce, industry sectors and activities. The importance of Pune as an industrial centre has grown rapidly since the 1960's when industrial expansion in Mumbai was banned. Consequently Pune has become a major centre in the state, having attracted heavy engineering industry such as motor vehicle manufacturing plants (buses, cars and motorcycles).

Definition of behavioural model as per oxford dictionary is “A model which takes into account the vagaries of human nature rather than depending on the concept of economic man”. There is no single theoretical framework in traffic psychology, but many specific models explaining the perceptual, intentional, cognitive, social, motivational and emotional determinants of mobility and traffic behaviour. R.J Brooks et al. (1978) develop the two estimates of the saturation level of car ownership, in order to illustrate how a small change in the specification of the model can result in substantially different values for the estimated saturation level and hence of car ownership forecasts. Both estimates are based on an

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2 Development of Behavioural Models of Travel for Metropolitan Areas Padmini G and S.L Dhingra (IIT Bombay) underlying logistic growth curve but different assumptions are made about error structure. Genevieve and Joyce (2005) conduct an international comparative analysis of relationships between car ownership, daily travel and urban form. Using travel diary data for the US and Great Britain, and estimates models of car ownership and daily travel distance. Both structural model with daily travel conditional upon car ownership and a reduced form model for daily travel, excluding car ownership, are estimated. Model results are similar, and show that differences in travel are explained by (1) differences in demographics between the two countries; (2) lower household income in Great Britain; (3) country specific differences in costs of car ownership and use, transport supply and other factors we have not been able to control. Joyce (2007) analyses the factors determining household car travel, and specifically the effects of household income and the prices of cars and motor fuels, and to explore the inter temporal pattern of adjustment. The question of asymmetry in the response to rising and falling income is also addressed. The impact of prices, the speed of adjustment and the resistance to change will be important in determining the possibility of influencing travel behaviour and specifically car use. The results are compared with those for car ownership estimated on the basis of similar models. Kumar and Rao (2006) conducted stated preference survey of car ownership in Mumbai Metropolitan Region (MMR) of Maharashtra in India. A full factorial experiment was designed to considering various attributes such as travel time, travel cost, projected household income, car loan payment and servicing cost. The car ownership alternatives considered 0, 1 and 2 cars. A multinomial logit framework was used to develop the car ownership model taking the household as a decision unit. The car ownership models developed in this study exhibit a satisfactory goodness of fit. It is concluded that the SP modeling approach can be successfully used for modeling car ownership decisions of households in developing countries. David and Train (1999) described and applied choice models, including generalizations of logit called mixed logits. The models were estimated on data from a stated-preference survey that elicited customers’ preferences among gas, electric, methanol, and compressed natural gas vehicles with various attributes. Comparison was made among different models like standard logit, mixed logit and probit models. Results indicate that household who place relatively little importance on price have a greater tendency to buy a larger car. Khan (1985) developed a model to describe the dynamics of transportation mode choice in which the interaction between transportation users and a public transportation authority results in self-organization. The model illustrates that a sufficient number of connections between a central city and its suburbs are required for self- organization to occur whereby public transportation use and service will grow. Sergio (1990) analyses the microeconomic foundations of mode choice models postulate modal utilities which are additive in income, this actually makes choice independent of this variables. On the other hand, it has been argued that income is correlated with variables that reflects taste and therefore, has a place in the utility specification as proxy for taste. In this study they proposed a framework based on a generalization of our expenditure rate approach in order to explore the presumptive relation between income and taste empirically. Chandra (2007) analyzed the entire choice continuum defining people’s lifestyles across temporal scales. This includes long term choices such as residential and work location choices, medium term choices such as vehicle and bicycle ownership, and short term choices such as mode choice and trip departure time choice. In recent times, there has

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3 Development of Behavioural Models of Travel for Metropolitan Areas Padmini G and S.L Dhingra (IIT Bombay) been increasing interest in modeling these choice dimensions simultaneously to account for endogenous nature of longer term location choices, residential self-selection effects, and simultaneity of choice processes that define a person’s lifestyle.

2.0 STUDY AREA, SURVEY DESIGN AND DATA ANALYSIS

2.1 Description of study Area: PUNE

The Pune city of Maharashtra State, India has been selected as study region to development of Revealed Preference (RP) vehicle ownership model, mode choice model. Delhi Metro Rail Corporation (DMRC) in Association with IIT-Bombay conducted traffic studies recently for the purpose of rider ship estimation on proposed . Details of delineation of study area, planning variables obtained from that study. The zoning system of the study area has been adopted from a previous study namely Integrated Traffic Dispersal System for PMC & PCMC carried out by CES in 2004. In addition to the CES zones a few new developments like Hinjewadi IT Park have been added as new zones. Some disaggregation has also been done in larger zones like . The zoning system of the study area for Metro study comprised 53 zones in the PMC area and 38 zones in PCMC area. Pune and cantonments have been considered as two zones. In addition to 91 internal zones, 13 external zones are considered. These external zones represent the catchment of external transport links feeding into the city. (DMRC and IIT-Bombay, 2008).

2.2 Survey design

A Home Interview Survey (HIS) was conducted at various localities in Pune by IIT Bombay. The data obtained from the home interview survey conducted by IIT Bombay in association with DMRC for the purpose of traffic forecast study for Pune metropolitan area. This survey contains mainly three parts namely; house hold information; person information and trip information. (DMRC and IITB, 2008) Home Interview survey is the most important survey for any transportation study of city. The household data for the development of model was derived from the HIS of Pune. Data obtaining from this HIS survey was used for developing the models. The HIS design developed attached in Appendix-1. Table 1 shows the number of samples collected income wise.

Table 1 Income wise Samples in PMC and PCMC of Pune City Number of data points in S.no Income(Rs) Number of data points in PMC PCMC

1 <=5000 229 198

2 5000-10000 925 378

3 10000-15000 516 96

4 15000-20000 238 29

5 20000-25000 96 11

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6 >=25000 269 12

Total 2273 724

2.3 Data analysis

The data contains around 3000 household samples that is around 1 % of total number of households in the study area. The first part of the survey having socio-economic characters like type of residence, ownership of residence, household size, number of people less than five years in family, number of cars, number of two wheelers, parking private, parking street. The second part of the survey data having person information like age, sex, education, occupation, type of work place, income, driving license, type of travel pass, cost of travel pass. The third part of the survey having trip information of all individuals in the house hold for previous day like origin, destination, type of work place, start time of journey, mode of travel, travel time, waiting time, travel cost, parking cost. Figure 1 shows model split of Pune obtained from HIS data.

TRAIN BICYCLE BUS 0.5% 8.8% 12.5%

WALK 25.0%

TW 38.6%

IPT 8.3% CAR 6.3%

Figure 1- Realized Modal Split from HIS

From HIS data was extracted and analysed. Table 2 shows the percentage of mode wise samples collected in PMC and PCMC. Table 3 shows analysis of collected samples in PMC and PCMC by different variables.

Table 2 Percentage of Mode wise samples in PMC and PCMC of Pune city Mode no Type of mode PMC (%) PCMC (%) Total Pune %wise 1 Walk 67.48 32.52 24.35 2 Cycle 79.66 20.34 9.69 3 TW 79.2 20.8 38.87 4 Car 84.41 15.59 4.67 5 Auto 89.01 10.99 6.34 6 Six seater 50.77 49.23 3.09 7 contract bus 82.29 17.71 2.16 8 PMT/PCMT 58.73 41.27 9.06 9 ST bus 50 50 0.15 10 Rail 25.93 74.07 0.43 11 Comp car 34.38 65.63 1.03 12 Car pool 33.33 66.67 0.17 Table 3 HIS data analysis of PUNE (PMC&PCMC)

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Table 3 HIS data analysis of PUNE (PMC&PCMC)

S.no Variable Range PMC (in %) PCMC (in %) ≤20 26.90 24.76 21-30 25.71 34.44 1 Age 31-40 19.89 18.94 41-50 12.96 11.09 >50 14.54 10.77 Male 54.31 55.10 2 sex Female 45.69 44.90 Own 84.47 79.92 3 House type Rented 13.82 17.10 Employer provided 1.71 2.99

≤5000 10.07 27.35 5000-10000 40.70 52.21 house hold 4 10000-15000 22.70 13.26 income 15000-20000 10.47 4.01 20000-25000 4.22 1.52 ≥25000 11.83 1.66 1 Household 1.89 1.36 2 Household 16.08 12.23 3 Household 32.07 25.54 5 House hold size 4 Household 31.13 29.62 5 Household 12.52 17.26 6 Household 3.56 8.70 7 Household 1.40 2.72 ≥8 Household 1.35 2.58 0 car 86.43 93.77 6 Car ownership 1 car 12.81 6.10 2 car 0.76 0.14 0 TW 18.61 27.61 Two wheeler 7 1 TW 69.58 62.64 ownership 2 TW 11.82 9.75

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3.0 METHODOLOGY, AND MODEL STRUCTURE

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3.1 Proposed Methodology

Collection of HIS Data

Extraction of required data from HIS

Data Verification & Logical checks

Development of RP Vehicle Ownership, Mode choice Model and shift models

Finding the significant variables

Specification of the Model

Calibration of Model No

Whether the Variables are Significant & the Model Goodness of Fit Satisfactory? Yes

Results and discussion

Checking validity of models (Prediction Success table)

The above flow chart shows the proposed methodology for developing the models

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3.2 Development of RP vehicle ownership Model

3.2.1 Data and Variables used in the model

All the specifications use subsets of the variables defined in Table 5.1. A large set of variables that are likely to influence car ownership were identified based on literature, expert opinion and statistical tests. The car ownership levels considered are zero, one and two car ownership levels. Simple MNL model was estimated by proper specification. As a starting point all the household and socioeconomic variables were used along with the mode-specific constants in defining the utility of different car ownership levels. The attention was given to the use of different mode-specific variables in utility function of different car ownership level. The variables were eliminated if found insignificant or having illogical signs. The variables so eliminated were then used in the utility function of other car ownership level and again the same checks were made. It was observed that on reaching the most optimal model specification, the absence of insignificant variables did not alter the value of coefficient estimates of remaining variables. The variables considered in this study are house hold size, type of residence, ownership of residence, parking private, driving license, parking cost. The variables household size, ownership of residence, parking private, driving license, parking cost are having logical significance and found to be significant. The other variables those are not significant are eliminated from the estimation.

Clean samples were obtained after doing logical checks and verification of raw data of 3000 households belonging to Pune city. The RP information on 2900 samples was used after corrections and logical checks in the raw data home interview survey for the development of RP model. All the model specifications use subsets of the attributes defined in Table 4.

Table 4 Variables used in the development of RP vehicle ownership model Variable code Description of variable HH Size House hold Size of the family Income Household income in thousands of rupees Ownership of residence owned-1,rented-2,employer OR provided-3 Type of residence Apartment-1,Independent house-2 TR common gallery-3, slum-4 PP Parking Private yes-1,no-0 PS Parking street yes-1,no-0 HH DL Driving license yes-1,no-0 PC Parking cost in rupees

3.3 Goodness-of-fit of models in terms of Prediction Success Table.

Though the models developed have a reasonable goodness-of-fit when measured by standard statistical tests, it is also desirable to test the performance of the models in

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9 Development of Behavioural Models of Travel for Metropolitan Areas Padmini G and S.L Dhingra (IIT Bombay) predicting the observed choice behaviour. A prediction success table can be used for testing this condition. Prediction success tables and indices were proposed originally by McFadden (1979). Prediction success table is a cross classification between the observed and predicted choice of alternatives. The probability of choice of each alternative is estimated for each observation in the data set using the calibrated utility functions. The alternative with the highest probability is considered the chosen alternative, and this is then compared with the alternative actually chosen. The prediction success table is then obtained by cross tabulating these predicted and observed values. (Ortuzar and Willumsen, 2006)

A prediction success table for a model can be developed as follows. Let the available data consist of observations of N individuals who had J alternatives to choose one among them. Let Pji denote the probability that individual i in the data set (i = 1,...., N) chooses alternative j (j = 1,..., J) as per the model under consideration. Let Sji equal 1 if individual is observed to choose alternative j and 0 otherwise. For each pair of alternatives (l, j) (l, j = 1…J) define Nlj as N N  S P lj  li ji Eq (1) i1 And define

nlj  Nlj / N Eq (2)

Then Nlj and nlj respectively represent the number and proportion of individuals in the data set who are observed to choose alternative l and predicted by the model to choose alternative j. Nll and nll respectively represent the number and proportion of individuals who are correctly predicted to choose alternative l. A prediction success table for the model is the J x J array whose (l,j) element is either Nlj or nlj. Either form of the table contains the same diagnostic information, or it is a matter of convenience which is used. In this, it will be convenient to use the form based on Nlj. In prediction success tables, column sums give predicted shares for the sample; row sums give observed shares. From prediction success table indices of prediction success by the model can be derived. Proportion of trips correctly predicted for the car l can be obtained as N C  ll l N Eq (3)  j lj Cl indicates that fraction of individuals expected to choose an alternative who do in fact choose that alternative. Overall proportion of choices successfully predicted by the model under consideration can be calculated as J N  j1 jj C  Eq (4) N

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4.0 MODEL RESULTS AND DISCUSSION

4.1 Results based on RP car ownership Model

From the RP model developed for car ownership it is observed that income which has a significant variable in vehicle ownership showing the double the significance in 2 car ownership when compared with one car ownership which is logically true. Table 5 depicts the output of the model developed. Other influencing variables like HH size and HH DL are more significant for 2 car ownership. In this model parking cost variable has high negative coefficient which is logically true. For developing all the models ALOGIT software was used. (Daly, 1992). All the variables used in the model have statistical and logical significance.

Table 5 Statistics and coefficient estimates of RP Car ownership Model t- Variable Coefficient standard error stat Relevance of variables HH Size 0.440 0.174 1.3 specific to 2 car Income 0.054 0.006 9.9 specific to one car TR -0.207 0.161 -1.3 specific to one car OR 0.228 0.251 1.9 specific to Zero car PP 0.176 0.132 1.4 specific to one car Income 0.088 0.011 7.5 specific to 2 car HH DL 1.007 0.523 1.9 specific to 2 car PC -0.675 0.353 -1.9 specific to 2 car constant 1 5.482 0.660 5.8 specific to Zero car constant 2 3.727 0.673 3.8 specific to one car Structural Parameters L (0) -3184.97 L (c) -1124.60 L (θ) -1008.50 ρ2 (0) 0.68 ρ2 (c) 0.10

4.2 Goodness-of-fit of RP Car ownership models in terms of Prediction Success Table.

The prediction success table for the car ownership model developed is given in Table 6. In this table rows have observed values columns have predicted values. The tabulated data indicate that the predictions are better in the case of zero car ownership and one car ownership. In the case of zero ownership level, 88.98 % choices were predicted correctly and in one car ownership level, 66.67 % choices were predicted correctly. In two and more than two car ownership level the prediction success was found to be very low due to negligible number of such choices in the sample. The overall percentage of prediction is 87.20. The diagonal elements of prediction success table shows the number of samples correctly predicted to that specific ownership.

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Table 6 Prediction Success Table for the RP Car Ownership Model 0 car 1 car 2 car Observed % Observed 0 car 2520 1 34 2555 88.13 1 car 299 4 23 326 11.25 2 car 13 1 4 18 0.62 Predicted 2832 6 61 2899 100 % Predicted 97.69 0.21 2.10 100.00 % Correctly predicted 88.98 66.67 6.56 Overall percentage of prediction 87.20

4.3 Results based on RP TW Ownership Model

In the study area Pune TW users are very high. As per home interview survey data the important mode of travel for Pune people is TW. Pune Metropolitan area has 19, 07,471 private vehicles as per RTO out of that more than 250 Two wheelers per 1000 population. Clean samples were obtained based on the logical checks and verification of data of 3000 households belonging to Pune city. The RP information on 2900 samples was used after corrections and logical checks in the raw data home interview survey for the development of RP model. The number of households with two wheeler ownership levels of 0, 1, 2 and more than 2 the samples are respectively 599, 1947, 329 and 21. The attributes used in development of car ownership models are used in the development of RP two wheeler ownership models. All the specifications use subsets of the variables defined in Table 4. A large set of variables that are likely to influence TW ownership were identified based on literature, expert opinion and statistical tests. The TW ownership levels considered are zero, one, two and more than 2 TW ownership levels. Simple MNL model was estimated by proper specification. The ρ-squared value was found to be 0.42. From the RP model developed for TW ownership it is observed that income which is significant variable in defining vehicle ownership showed a negative sign coefficient for one TW ownership which is the opposite of generally it should be, this can be explained for the scenario of vehicle loans installments given in Pune for buying TW thus people having lesser income also have opted to buy a TW to get good ease of accessibility because of not so good Public transport in Pune. In this model also HH size and HH DL have more significant values. Table 7 gives the output of the model which depicts the statistics and coefficient estimates of the model.

Table 7 Statistics and coefficient estimates of RP TW ownership Model standard Variable Coefficient error t-stat Relevance of variables HH Size 0.211 0.042 5.1 specific to 2 TW Income 0.020 0.009 2.1 specific to 2 TW OR -0.407 0.093 -4.4 specific to 1 TW HH DL 0.196 0.077 2.5 specific to 1 TW TR 0.393 0.040 9.6 specific to Zero TW PP 1.448 0.087 16.6 specific to one TW

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HH 0.430 0.110 3.9 specific to more than 2TW HH DL 0.4658 0.102 4.6 specific to 2 TW constant 1 4.498 0.599 7.5 specific to Zero TW constant 2 6.347 0.596 10.6 specific to one TW constant 3 1.696 0.602 5.6 specific to two TW Structural Parameters L (0) -4002.23 L (c) -2516.31 L (θ) -2272.51 ρ2 (0) 0.43 ρ2 (c) 0.10

4.4 Goodness-of-fit of RP TW ownership models in terms of Prediction Success Table.

The prediction success table for the TW ownership model developed is given in Table 8. Tabulated data indicates that the predictions are better in the case of zero, one and two TW ownership. In the case of zero TW ownership level, 56.39% choices were predicted correctly and in one TW ownership level, 79.86% choices were predicted correctly. In two TW 94.52 choice predicted correctly and more than two TW ownership level prediction success was found to be low due to negligible number of such choices in the sample. The overall percentage of prediction success table is 79.

Table 8 Prediction Success Table for the RP TW ownership Model 0 TW 1 TW 2 TW 3 TW Observed % Observed 0 TW 177 419 10 2 608 21.06 1 TW 121 1815 1 18 1955 67.72 2 TW 3 11 309 0 323 11.19 3 TW 0 0 0 1 1 0.03 Predicted 301 2245 320 21 2887 100 % Predicted 10.43 77.76 11.08 0.73 100 % Correctly predicted 58.80 80.85 96.56 4.76 Overall prediction success rate 79.74

4.5 Development of Mode Choice Model

Total numbers of raw samples obtained from trip information are around 12000 samples. After doing logical checks and refinement analysis around 6500 samples are considered for the development of RP mode choice model. Before developing the RP mode choice model we need attributes for mode wise, for that assignment was done on Pune network using CUBE (TRIPS) software’s and travel skims were obtained for cost and time for PT and PV. In trip information part of HIS data, socio economic factors are not available socio economic characters were available in person information. C-Program was developed for getting the correlation between trip information and person information from HIS data of

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Pune. The data base for mode choice was prepared based on these skims available from the CUBE (TRIPS) and RP data obtained HIS.

Simple MNL model was estimated by proper specification. As a starting point all the system and socioeconomic variables were used along with the mode-specific constants in defining the utility of different modes. After identifying the significant variables finally multinomial logit model was developed using significant variables explaining mode choice along with mode specific variables. Variables were eliminated if found insignificant or illogical. The variables so eliminated were then used in the utility function of other mode and again the same checks were made. It was observed that on reaching the most optimal model specification, the absence of insignificant variables did not alter the value of coefficient estimates of remaining variables. The software ALOGIT is used to estimate the parameters of this model. The statistics and coefficient estimates for the RP model are presented in Table 9.

Table 9 Statistics and coefficient estimates of RP mode choice Models Variable Coefficient standard error t-stat Relevance of variables TT -0.034 0.011 -3.0 specific to walk mode Age -0.012 0.003 -4.4 specific to walk mode TC -0.031 0.007 -4.6 specific to TW mode Income 0.070 0.012 5.9 specific to TW mode DL 1.406 0.094 14.9 specific to TW mode TT 0.064 0.006 10.5 specific to Car mode TC 0.016 0.004 4.4 specific to Car mode Income 0.090 0.014 6.7 specific to Car mode DL 1.169 0.153 7.7 specific to Car mode WT -0.121 0.007 -18.2 specific to PT mode TT -0.008 0.002 -3.7 specific to PT mode TC -0.046 0.005 -9.4 specific to PT mode constant1 1.150 0.221 5.2 specific to walk mode constant2 -1.919 0.163 -11.8 specific to TW mode constant3 -3.396 0.203 -16.8 specific to Car mode L (0) -7559.463 L (c) -6336.828 L (θ) -4568.465 ρ2 (0) 0.396 ρ2 (c) 0.279

From the RP model developed for mode choice it is observed that for walk mode age, TT variable coefficients have negative sign, it indicates older people are not preferring walk mode and at the same time if travel time is more people are not preferring the mode which is logically true. In public transport mode WT, TT and TC coefficients have negative sign which clearly indicates that people are neither willing to wait nor willing to pay more to make their

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14 Development of Behavioural Models of Travel for Metropolitan Areas Padmini G and S.L Dhingra (IIT Bombay) trip. Surprising fact observed from the model is that in car utility equations TT, TC coefficients are showing positive sign it indicates they are willing to travel by car only even though it is more travel time and travel cost. The main reason of choosing car mode even it is more TT and TC is due to less frequency and improper facilities of PT. The RP mode choice models developed and calibrated in this study exhibit good results in terms of goodness-of-fit measures. The ρ-squared value was found to be 0.396.

4.6 Goodness-of-fit of RP mode choice models in terms of Prediction Success Table.

The prediction success table for the mode choice model developed is given in Table 10. Tabulated data indicates that the predictions are better in the case of walk, TW, PT. In the case of walk choice, 57.62 % choices were predicted correctly, in TW mode choice, 67.90% choices were predicted correctly and in PT choice 59.64% choices were predicted correctly and in case of car choice level prediction success were found to be low. The overall percentage of prediction success is 64.36.

Table 10 Prediction Success Table for the RP Mode choice Model

walk TW car PT Observed % Observed walk 348 357 0 251 956 15.34 TW 99 2801 125 186 3211 51.51 car 26 275 102 78 481 7.72 PT 131 692 2 761 1586 25.44 Predicted 604 4125 229 1276 6234 100 % Predicted 9.69 66.17 3.67 20.47 100 % Correctly predicted 57.62 67.9 44.54 59.64 Overall prediction success rate 64.36

4.7 Conclusions

Problem definition, need of this study, some important literature which is related to the work, proposed methodology and development of models are discussed in above chapters. From the developed models and data analysis some conclusions are drawn which are given below.

 HIS data was used as raw data for developing the models. Before considering the HIS data clear analysis and logical checks have been done.  The existing modal share of private vehicles in study area Pune is around 67%, out of which more are TW, but the potential shift to public transport has not been captured by mode choice model. Because of poor public transport facilities in Pune people are willing to travel by private vehicles even though it is more travel time and travel cost.

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 Analysis of vehicle demand, discrete-choice models, such as multinomial and nested logit models, permit a disaggregate level of analysis. These models are generally more behavioral in nature and more policy sensitive.  This paper discuses the development of vehicle ownership models and mode choice models. Over all three models are developed.  RP models developed for vehicle ownership are showing satisfactory goodness of fit in car ownership overall prediction is 87.20%, in TW ownership overall prediction is 79.74% and in mode choice overall prediction is 64.36%.  Mode choice data base was developed based on some, RP data from HIS assumptions and skims obtained from the network developed in CUBE software for Pune metro.  RP mode choice model was developed by multinomial logit model specification.  This paper contributes the development of models with RP data. If SP data available, development of combined RP and SP estimation models by using mixed estimation procedure may gives robust models.

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REFERENCES

Chandra R Bhat (2007) “Multidimensional integrated choice model”, [Accessed on 5 th july, 2008] Available from world wide Web:

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David Brownstone and Kenneth Train (1999). "Forecasting new product penetration with flexible substitution patterns”. Journal of Econometrics, vol.89, 109-129.

Genevieve Giuliano and Joyce Dargay (2005). “Car ownership, travel and land use: a comparison of the US and Great Britain”. Transportation Research Part A, vol.40 106- 124

Joyce Dargay (2007) “The effect of prices and income on car travels in the UK”. Transportation Research Part A, vol. 41, 949–960.

Khan, A. M. (1985), “Toward the Development of Innovative Models of Intercity Travel Demand,” Transportation Quarterly, vol.39 (2), 297-316.

Kumar M and Rao K.V.K (2006) “A Stated Preference Study for a Car Ownership Model in the Context of Developing Countries” Transportation Planning and Technology, Vol.29, Issue 5, 409 – 425

Ortuzar, J. de D and Willumsen, L. G. (2006). “Modelling Transport”. Third Edition, John Wiley & Sons, Chichester.

Pune metro draft final report (2008). “Traffic Forecast for the Proposed Metro Rail Project in Pune Metropolitan Area”, DMRC association with IIT Bombay, India.

R.J Brooks, A.P.Dawid, J.I.Galbraith and M.Stone (1978). “A note on forecasting car ownership” Journal of the Royal Statistical Society. Series A (General), Vol.141, No. 1, 64-68

Sergio R. Jara-d-faz (1990) “Income and taste in mode choice models: are they surrogates?” Transportation research part B, vol. 25, 341-350.

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17 Development of Behavioural Models of Travel for Metropolitan Areas Padmini G and S.L Dhingra (IIT Bombay)

APPENDIX I

12th WCTR, July 11-15, 2010 – Lisbon, Portugal

18 Development of Behavioural Models of Travel for Metropolitan Areas Padmini G and S.L Dhingra (IIT Bombay)

12th WCTR, July 11-15, 2010 – Lisbon, Portugal

19 Development of Behavioural Models of Travel for Metropolitan Areas Padmini G and S.L Dhingra (IIT Bombay)

12th WCTR, July 11-15, 2010 – Lisbon, Portugal

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