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Research Article | Open Access

Analysis and modeling of residents travel behavior under MaaS

Daqian Wan, Xumei Chen(), Yuqing Wang

Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing, 100044 China

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Abstract

The swift advancement of urban motorization has resulted in a disparity between the supply and demand of traffic services. The conventional travel service framework has proven inadequate in facilitating a seamless integration of various transportation modes, thereby rendering travel inconvenient for urban residents. Mobility as a Service (MaaS) represents an innovative transportation solution that offers quicker, more efficient, and cost-effective travel alternatives. Nonetheless, the effectiveness of the MaaS system is contingent upon travelers’ acceptance and their perceived preferences regarding MaaS. Consequently, this study categorizes travel choice modes within the context of MaaS and develops multinomial logit (MNL) models. Acknowledging the limitations inherent in MNL models, this research proposes enhancements by incorporating random parameters to formulate a mixed logit (MXL) model. A comparative analysis of the parameter estimation outcomes from both the MNL and MXL models reveals an improved interpretative capacity when accounting for heterogeneity. Furthermore, the findings derived from the MXL model indicate that latent influencing factors, such as punctuality, significantly impact travel choices within the MaaS framework. Theoretically, this study offers a valuable reference for quantifying the potential perceptions associated with MaaS travel modes. Practically, the classification of potential travelers aids in identifying early adopters of MaaS and facilitates targeted promotion of MaaS to specific demographic groups, thereby mitigating resistance to its implementation.

Keywords

intelligent transport MaaS travel behavior multiple logit model mixed logit model influencing factors

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Journal of Highway and Transportation Research and Development (English Edition)

Volume 18 Issue 4,
December 2024

Pages 16-23

DOI: 10.26599/HTRD.2024.9480034

Cite this article:

Wan D, Chen X, Wang Y. Analysis and modeling of residents travel behavior under MaaS. Journal of Highway and Transportation Research and Development (English Edition), 2024, 18(4): 16-23. https://doi.org/10.26599/HTRD.2024.9480034

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Table 1Variable setting

Class	Variable	Definition	Value
Dependent variable	Y	Preferred mode of travel to choose MaaS	1, Private car, 2, Bus+subway, 3, Bus+Subway+Taxi, 4, Shared Bike+Bus + Subway, and 5, Subway +Taxi
Individual socioeconomic attributes	X₁	Gender	1, Male and 2, Female
	X₂	Age	1, 18 to under, 2, 18 to 25, 3, 26 to 30, 4, 31 to 40, and 5, 41 and over
	X₃	Occupation	1, Student, 2, Institution staff, 3, Freelancer, 4, Service staff, and 5, Enterprise staff
	X₄	Education level	1, Junior high School and below, 2, High school, 3, Junior College, 4, Bachelor’s, 5, Master, and 6, Doctor
Scene factor	X₅	Monthly income	1, Below 5000 yuan, 2, CNY5000 to CNY7000 yuan, 3, CNY7000 to CNY9000 yuan, 4, CNY9000 to CNY11000, and 5, CNY11000 or more
	X₆	Number of private cars	1, None, 2, 1 Cars, 3, 2 Cars, and 4, 3 Cars and above
	X₇	Monthly living expenses	1, Less than CNY1000 yuan, 2, CNY1000 to CNY2000, 3, CNY2000 to CNY4000, 4, CNY4000 to CNY6000, 5, CNY6000 to CNY10000, and 6, more than CNY10000
	X₈	Monthly traffic expenditure	1, CNY100 or less, 2, CNY100 to CNY300, 3, CNY300 to CNY500, 4, CNY500 to CNY1000, and 5, CNY1000 or more
	X₉	MaaS advantage	1, convenience, 2, punctuality, 3, comfort, and 4, economy
	X₁₀	Be willing to try new things	1, not willing, 2, basically not willing, 3, fair, 4, willing, and 5, more willing
	X₁₁	What weather will choose MaaS	1, bad weather, 2, fair weather, and 3, comfortable weather
	X₁₂	When will MaaS be selected	1, early morning, 2, morning, 3, afternoon, and 4, night
	X₁₃	What travel purpose will choose MaaS	1, school, 2, work, 3, business, 4, travel and recreation, and 5, visiting friends and family
	X₁₄	Reasons for choosing MaaS	1, price, 2, weather, 3, time, 4, distance, and 5, environmental protection

Table 2Parameter estimation

Y	Regression coefficient	Standard deviation	z	$χ^{2}$	p	OR
Occupation	−0.272	0.275	−0.990	0.980	0.322	0.762
Educational level	0.433	0.217	1.996	3.982	0.046	1.542
Monthly income	0.752	0.415	1.812	3.284	0.070	2.121
Private car	−0.162	0.391	−0.414	0.172	0.679	0.850
Cost of living	−0.401	0.293	−1.369	1.874	0.171	0.670
Traffic overhead	−0.395	0.295	−1.336	1.786	0.181	0.674
Number of trips	0.394	0.196	2.006	4.025	0.045	1.482
Understanding degree	0.055	0.166	0.331	0.109	0.741	1.057
Intercept	−1.028	1.197	−0.858	0.737	0.391	0.358

Table 3Parameter estimation

Y=2	Standard deviation	z	χ²	p	OR
Education level	0.213	1.947	4.134	0.032	1.012−1.918
Price	0.462	2.467	7.556	0.004	1.489−9.582
Distance	0.478	0.982	0.783	0.250	0.531−3.290
Punctuality	0.653	2.012	3.562	0.000	1.234−5.782

Table 4Likelihood ratio test

	Logarithmic likelihood value	$χ^{2}$	$ρ^{2}$	p	AIC	BIC
Intercept	−513.303
Final model	−482.439	73.864	0.327	0.000	854.439	1003.906

Table 5Likelihood ratio test

Cox and Snell	Nagelkerke	McFadden
0.393	0.422	0.594

Table 6Significant influencing factors

	Y=2	Y=3	Y=4	Y=5
Education level	0.349*	0.421*	0.326	0.833*
Rain and snow weather	1.118*	1.940*	0.836	1.717*
Price	1.351*	1.274*	1.062*	1.774*
Distance	−0.363	−1.250*	−1.262*	−0.839
Intercept	−1.102	−0.958	−1.425	−2.146
Likelihood ratio test	χ²(16)=73.864, p=0.000
Significance levels: - not significant, 0.05, *0.01.

Table 7Comparison results of model parameter estimation

Influencing factors	MNL parameter				MXL parameter
Influencing factors	Y=2	Y=3	Y=4	Y=5	Y=2	Y=3	Y=4	Y=5
Intercept	−1.102	−0.958	−1.425	−2.146	−1.034	−0.798	−1.305	−1.989
Education level	0.349*	0.421*	0.326	0.833*	0.284	0.321	0.282*	0.725*
Rain and snow weather	1.118*	1.940*	0.836	1.717*	1.017	1.826*（0.825）	0.792*	1.651
Price	1.351*	1.274*	1.062*	1.774*	1.218	1.149	0.957*	1.678*
Distance	−0.363	−1.250*	−1.262*	−0.839	−0.429*	−1.410	−1.382*	−0.937
Punctuality	0.852	1.049*	0.581	0.967	0.801*	0.957*	0.512*	0.863*
Logarithmic likelihood Value	−508.636				−482.439
AIC	889.636				854.439
BIC	1021.875				1003.906