The results in the column “Change in probability” show the change in the probability of choosing a mode of transport when the time on the vehicle changes by 1 minute. The results are all negative, indicating that when the time on the vehicle increases, the probability of choosing a mode of transport decreases. The data also shows that when the time on the vehicle changes, it affects the choice of modes of transport, in which the strongest impact is on the choice of motorbike, followed by the choice of the new mode of transport, which is the elevated railway.
b. The influence of time outside the vehicle
The results are all negative, indicating that as time spent outside of the vehicle increases, the probability of choosing a mode of transport decreases. The data also shows that as time spent outside of the vehicle changes, it affects the choice of transport modes, with the strongest impact on the choice of motorbike, followed by the choice of bus, bicycle and the new mode of elevated railway.
Table 4.22 Results of marginal variation analysis of off-vehicle time
Method
Change probability | Standard error | Z-Statistic | P>Z | |
MOTORBIKE | -0.277073 | 0.014213 | -19.49 | 0.000 |
BUS | -0.029424 | 0.002305 | -12.77 | 0.000 |
BICYCLE | -0.024733 | 0.002181 | -11.34 | 0.000 |
METRO | -0.021995 | 0.000572 | -38.44 | 0.000 |
OTHER | -0.011638 | 0.001366 | -8.52 | 0.000 |
TAXI | -0.008878 | 0.001133 | -7.83 | 0.000 |
CAR | -0.007606 | 0.000965 | -7.88 | 0.000 |
WALK | -0.001356 | 0.000201 | -6.73 | 0.000 |
MOTORBIKE TAXI | -0.000442 | - | - | - |
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c. Impact of travel costs
The data show that the impact of trip cost has the largest impact on the choice of motorbike, followed by elevated railway. The other modes are affected by cost but the impact is small. The results of the variation confirm
The probabilities are all negative, consistent with theory, meaning that as trip costs increase, the probability of choosing a mode of transport decreases.
Table 4.23 Results of marginal variation analysis of trip cost
Method
Change probability | Standard error | Z-Statistic | P>Z | |
MOTORBIKE | -0.0086130 | 0.0004800 | -17.95 | 0.000 |
METRO | -0.0074690 | 0.0004270 | -17.50 | 0.000 |
BUS | -0.0009150 | 0.0000830 | -11.05 | 0.000 |
BICYCLE | -0.0007690 | 0.0000710 | -10.77 | 0.000 |
OTHER | -0.0003620 | 0.0000440 | -8.28 | 0.000 |
TAXI | -0.0002760 | 0.0000220 | -12.69 | 0.000 |
CAR | -0.0002360 | 0.0000300 | -7.83 | 0.000 |
WALK | -0.0000420 | 0.0000065 | -6.44 | 0.000 |
MOTORBIKE TAXI | -0.0000140 | - | - | - |
d. Effect of cost to income ratio
Similar to the effects of travel time and cost, the cost-to-income ratio has the largest effect on the probability of choosing a motorbike, followed by the elevated railway. The third most affected is the bus. The results of the change in probability are all negative, indicating that as the cost-to-income ratio increases, the probability of choosing the mode of transport decreases.
Table 4.24 Results of marginal variation analysis of cost-to-income ratio
Method
Change probability | Standard error | Z-Statistic | P>Z | |
MOTORBIKE | -1.314330 | 0.273995 | -4.80 | 0.000 |
METRO | -1.139780 | 0.237262 | -4.80 | 0.000 |
BUS | -0.139577 | 0.030897 | -4.52 | 0.000 |
BICYCLE | -0.117324 | 0.026484 | -4.43 | 0.000 |
OTHER | -0.055208 | 0.013043 | -4.23 | 0.000 |
TAXI | -0.042112 | 0.009301 | -4.53 | 0.000 |
CAR | -0.036079 | 0.008609 | -4.19 | 0.000 |
WALK | -0.006432 | 0.001587 | -4.05 | 0.000 |
MOTORBIKE TAXI | -0.002098 | - | - | - |
e. Impact of income
The results table shows the change in the probability of choosing a mode of transport when income changes by 1 thousand VND. The effect of income on the probability of choosing a mode of transport according to the calculation results shows that: the group with strong impact includes: motorbike, bicycle, bus, elevated railway; the group with less impact includes: car, taxi, motorbike taxi and other modes. This result does not show a clear impact of income on the form of walking.
Table 4.25 Results of the analysis of marginal variation of income
Method
Change probability | Standard error | Z-Statistic | P>Z | |
MOTORBIKE | 0.000009100 | 0.00000170 | 5.50 | 0.000 |
BICYCLE | -0.000009000 | 0.00000043 | -21.15 | 0.000 |
BUS | -0.000004400 | 0.00000049 | -8.95 | 0.000 |
METRO | 0.000004200 | 0.00000160 | 2.61 | 0.009 |
OTHER | -0.000001700 | 0.00000030 | -5.66 | 0.000 |
CAR | 0.000001400 | 0.00000015 | 9.03 | 0.000 |
TAXI | 0.000000500 | 0.00000013 | 3.83 | 0.000 |
MOTORBIKE TAXI | -0.000000075 | - | - | - |
WALK | -0.000000014 | 0.00000002 | -0.69 | 0.488 |
f. Impact of opportunities to use personal vehicles
Table 4.26 Results of the analysis of marginal variation in the opportunity to use personal vehicles
Method
Change probability | Standard error | Z-Statistic | P>Z | |
MOTORBIKE | 0.109858 | 0.018461 | 5.95 | 0.000 |
METRO | -0.084987 | 0.017887 | -4.75 | 0.000 |
BUS | -0.046150 | 0.005029 | -9.18 | 0.000 |
CAR | 0.019047 | 0.001952 | 9.76 | 0.000 |
BICYCLE | 0.018565 | 0.003600 | 5.16 | 0.000 |
TAXI | -0.014574 | 0.001993 | -7.31 | 0.000 |
WALK | -0.002503 | 0.000378 | -6.62 | 0.000 |
MOTORBIKE TAXI | -0.001585 | - | - | - |
OTHER | 0.002329 | 0.003464 | 0.67 | 0.501 |
The effect of the opportunity to use personal vehicles according to the calculation results is consistent with the theory, the signs of the results show that when the opportunity to use personal vehicles increases, the probability of choosing bicycles, motorbikes and cars increases, while the probability of choosing public and semi-public transport decreases. However, the results do not show the effect of the opportunity to use personal transport on the probability of choosing the "Other" mode of transport.
4.3.3 Impact of motorbike usage opportunities on public transport mode choice
Currently, the number of trips by motorbike accounts for a large proportion of the total number of daily trips of people and motorbikes are considered one of the main causes of traffic congestion. To assess the impact of the opportunity to use motorbikes on the probability of choosing a transport mode, the variable CM (describing the opportunity to use motorbikes) is separated from the variable CH and XEMAY is chosen as the basic attribute or comparison criterion in the model, the results are shown in Table 4.27.
All parameters associated with the CM variable for transport modes have negative signs, indicating that when the opportunity to use motorbikes increases, the probability of choosing motorbikes will increase relative to the probability of choosing the remaining transport modes, and obviously when the opportunity to use motorbikes decreases, this probability will decrease. Thus, according to the results obtained from the model, if there are reasonable solutions to limit the use of motorbikes, the proportion of trips by motorbikes will decrease and the proportion of trips by other transport modes will increase.
Table 4.27 Results of the MH METRO-Base-XEMAY model
logit | Number of obs | = | 111888 | |
Case variable: IDtrip | Number of cases | = | 12432 | |
Alternative variant: PHUONGTHUC | Alts per case: min | = | 9 | |
avg | = | 9.0 | ||
max | = | 9 | ||
Wald chi2(20) | = | 3946.07 | ||
Log likelihood = -12906.295 | Prob > chi2 | = | 0.0000 |
-------------------------------------------------- ----------------------------
LC | Coef. Std. Err. zP>|z| [95% Conf. Interval]
-------------+------------------------------------------------------------- METHOD |
TGT | -.1166965.002966 | -39.34 | 0.000 | -.1225097 | -.1108832 | |||
TGN | -1.262047 | .062818 | -20.09 | 0.000 | -1.385168 | -1.138926 | ||
CP | -.0396217 | .0022495 | -17.61 | 0.000 | -.0440306 | -.0352128 | ||
CT | -7.426039 | 1.31045 | -5.67 | 0.000 | -9.994474 | -4.857603 | ||
-------------+------------------------------------ ---------------------------- | |||||||
DIBO | | | ||||||
TN | -.0000229 | .0000183 | -1.25 | 0.211 | -.0000587 | .000013 | ||
CM | -2.476635 | .2036476 | -12.16 | 0.000 | -2.875777 | -2.077494 | ||
_cons | 2.73571 | .1536292 | 17.81 | 0.000 | 2.434602 | 3.036818 | ||
-------------+------------------------------------ ---------------------------- | |||||||
OTHER | | | ||||||
TN | -.0001247 | .0000374 | -3.34 | 0.001 | -.000198 | -.0000514 | ||
CM | -2.73013 | .3702996 | -7.37 | 0.000 | -3.455904 | -2.004356 | ||
_cons | -1.140959 | .2584068 | -4.42 | 0.000 | -1.647427 | -.6344911 | ||
-------------+------------------------------------ ---------------------------- | |||||||
METRO | | | ||||||
TN | .0000111 | 8.74e-06 | 1.27 | 0.206 | -6.07e-06 | .0000282 | ||
CM | -1.226004 | .1020559 | -12.01 | 0.000 | -1.42603 | -1.025979 | ||
_cons | 12.12.2015 | .6357549 | 19.06 | 0.000 | 10.87409 | 13.36621 | ||
-------------+------------------------------------ ---------------------------- | |||||||
OTO | | | ||||||
TN | .0002771 | .0000145 | 19.11 | 0.000 | .0002487 | .0003055 | ||
CM | -.6271784 | .3658749 | -1.71 | 0.086 | -1.34428 | .0899233 | ||
_cons | -4.317707 | .3290125 | -13.12 | 0.000 | -4.96256 | -3.672854 | ||
-------------+------------------------------------ ---------------------------- | |||||||
TAXI | | | ||||||
TN | | .0000469 | .0000179 | 2.63 | 0.009 | .0000119 | .0000819 | |
CM | | -2.21092 | .2022136 | -10.93 | 0.000 | -2.607252 | -1.814589 | |
_cons | | 1.187137 | .1721961 | 6.89 | 0.000 | .8496385 | 1.524635 | |
-------------+------------------------------------ ---------------------------- | |||||||
BUS | | | ||||||
TN | -.0001822 | .0000237 | -7.69 | 0.000 | -.0002287 | -.0001358 | ||
CM | -2.407256 | .2328483 | -10.34 | 0.000 | -2.86363 | -1.950882 | ||
_cons | 37.87775 | 1.897837 | 19.96 | 0.000 | 34.15806 | 41.59745 | ||
-------------+------------------------------------ ---------------------------- | |||||||
XEDAP | | | ||||||
TN | -.0003332 | .0000223 | -14.95 | 0.000 | -.0003769 | -.0002895 | ||
CM | -3.869335 | .199398 | -19.41 | 0.000 | -4.260148 | -3.478522 | ||
_cons | 2.113294 | .1289034 | 16.39 | 0.000 | 1.860649 | 2.36594 | ||
-------------+------------------------------------ ----------------------------
SEE | (base alternative)
-------------+------------------------------------------------------------- XEOM |
TN | -.0001984 .0001035 -1.92 0.055 -.0004012 4.39e-06
CM | -6.289812 .8755357 -7.18 0.000 -8.005831 -4.573794
_cons | .298146 .5031399 .59 .553 -.6879901 1.284282
-------------------------------------------------- ----------------------------
4.4 Some recommendations
- Using the MH METRO model to predict the probability of choosing the mode of transport of trip takers in Ho Chi Minh City.
- Because the cost-to-income ratio affects the probability of choosing the mode of transport of people making trips in Ho Chi Minh City, to improve the situation of using public transport in the city, it is necessary to have measures to reduce the fare of public transport.
- To reduce the rate of choosing motorbikes compared to other means of transport, solutions are needed to limit people's opportunities to use motorbikes.
- According to the research results, the time spent outside the vehicle also affects the decision to choose the mode of transport of the traveler, the longer the time, the more obstacles it causes in the decision to choose the mode of transport. Therefore, measures are needed to increase the density of the public transport network to reduce the time spent outside the vehicle, thereby attracting more people to switch to using public transport.
Chapter 4 Conclusion
Chapter 4 of the thesis analyzed the current situation of travel demand of people making trips in Ho Chi Minh City, thereby showing the main characteristics of travel demand of city residents. Based on the analysis of the results obtained from the linear regression model in chapter 3, the author has evaluated and selected the main factors influencing the decision to choose the mode of transport of people making trips and used these factors to build a forecast model for the allocation of travel demand to modes of transport.
The influence of the factors is further studied in more detail in the multinomial logit model to determine the probability of choosing the mode of transport of the person making the trip in Ho Chi Minh City.
The calculation of the multinomial Logit model on the data set provided by SUD company aims to re-verify the research hypotheses on influencing factors and determine the model to predict the probability of choosing the mode of transport for people making trips in Ho Chi Minh City in the case of taking into account the appearance of new modes of transport.
After the research process of improvement and adjustment, the results of chapter 4 provide a model for predicting the probability of choosing a mode of transport for people making trips within the city. The research to expand the model in the case of the appearance of a new mode of transport, Metro, is also conducted simultaneously. The results of estimating the model for predicting the choice of a mode of transport for people making trips to Ho Chi Minh City in the condition of the appearance of a new mode of transport show that this model can be used for future forecasting.
Through the research results, chapter 4 of the thesis also proposes some recommendations when using this forecasting model.
CONCLUDE
Research on forecasting the distribution of travel demand for modes of transport in urban areas in Vietnam is an inseparable task from transport planning. The current problems of urban transport in Vietnam such as traffic congestion, pollution, and noise cause many negative consequences that people have to bear and require to be resolved. The main cause of the problem is due to the rapid mechanical growth rate of urban population, exceeding the response capacity of transport infrastructure. Another reason is that previous travel demand forecasting models may not reflect clearly and accurately the fluctuations in travel demand in general and travel demand for each mode of transport in particular. In this context, the thesis studies the influencing factors and builds a forecasting model for the distribution of travel demand for modes of transport in urban areas suitable for the actual conditions in Vietnam.
The thesis has some new contributions specifically shown as follows:
- For the four-step travel demand forecasting model used in the thesis, the author systematizes the forecasting methods for travel demand allocation for each mode of transport, evaluates the advantages and disadvantages of each method as a basis for selecting a forecasting model. In addition, the thesis also evaluates the factors affecting the decision to choose a means of transport of traffic participants in the world in general and in Vietnam in particular and proposes a forecasting model suitable for conditions in Vietnam.
- Based on the assessments and collected data, the thesis reveals the reasons for choosing the model to forecast the distribution of travel demand for each mode of transport in large cities in Vietnam.
- The model predicting the probability of choosing the mode of transport of the trip taker (specifically applied to Ho Chi Minh City) has taken into account the appearance of a new mode of transport, the elevated railway (Metro).
Besides the achieved results, the thesis still has some shortcomings that need further research: