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Exploring Impact of COVID-19 on Travel Behavior

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Abstract

Since its outbreak in December 2019, COVID-19 has spread rapidly across the world. To slow down the spread of the pandemic, various countries have implemented a series of policies and measures. The transportation system is not only an important carrier for COVID-19, but also a vital means for the prevention and control of the spread of the pandemic. Therefore, most anti-pandemic measures are based on travel restrictions, thereby slowing down the spread of the pandemic. As a result, because of the impact of the pandemic and corresponding control measures, the transportation system has undergone tremendous changes. By analyzing the evolution of the transportation system in response to the influence of COVID-19, it is possible to better understand socioeconomic changes and the changes in residents' daily life. Based on rich license plate recognition data, the characteristics of urban motorized travel under the influence of COVID-19 has been analyzed. According to the processes associated with the control of the pandemic and the resumption of work and production, the analysis period is divided into four stages. The changes in indicators of macroscopic traffic status are analyzed for each stage. The three types of typical motor vehicle groups (i.e., non-localized operating vehicles, taxis, and localized operating vehicles) are characterized by the traffic flow they contribute, the number of vehicles in transit, the average travel intensity, the average daily travel time of a vehicle, the average daily travel distance of a vehicle, and the spatiotemporal distributions of origins and destinations of trips. These data clarify the spatiotemporal evolution characteristics of peoples’ travel behavior at different stages of the pandemic. The results of data analysis show that COVID-19 has deeply changed the motorized travel behavior of urban residents. In the initial stage of resumption of work and production, the willingness to engage in motorized travel had decreased significantly compared with that in the first stage. This willingness gradually resumed until the third and fourth stages, but still did not fully reach the level before the onset of the pandemic. Specifically, the traffic status during morning and evening peaks has basically recovered, and has even increased beyond the level before the pandemic; however, a certain gap was still found between off-peak hours. There were also significant differences in the extent to which different types of vehicles were affected by the pandemic. Among these, taxis were impacted the most by the pandemic. In the fourth stage (at the end of April), the average daily travel time of a vehicle and the average daily travel distance of a vehicle still decreased by 29.25% and 22.63% compared with the first stage, respectively. The operating time of many taxis was shortened from 22:00 PM to 19:00 PM. The spatiotemporal characteristics of vehicles show that the reduction of flexible travel demand (e.g., shopping, catering, and entertainment) is key to the reduction of the travel demand of the road network. This research provides data support for the implementation of traffic control measures under future grave public health events and enables the formulation of urban traffic policies in the post COVID-19 era.

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Data Availability Statement

All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

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Funding

This study was supported by "Pioneer" and "Leading Goose" R&D Program of Zhejiang (2022C01042), the National Natural Science Foundation of China (Grant No. 72361137006 and 92046011), the Natural Science Foundation of Zhejiang Province (Grant No. LR23E080002), the Scientific Research Fund of Zhejiang University (XY2023040).

Author information

Authors and Affiliations

  1. Institute of Intelligent Transportation Systems, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, 310058, China

    Wenbin Yao, Congcong Bai, Sheng Jin & Chengcheng Yang

  2. Center for Balance Architecture, Zhejiang University, Hangzhou, 310058, China

    Wenbin Yao, Congcong Bai, Sheng Jin & Chengcheng Yang

  3. Zhejiang Lab, Hangzhou, 311121, China

    Youwei Hu

  4. Zhongyuan Institute, Zhejiang University, Zhengzhou, 450000, China

    Sheng Jin

Authors
  1. Wenbin Yao
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  2. Youwei Hu
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  3. Congcong Bai
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  4. Sheng Jin
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  5. Chengcheng Yang
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Contributions

Conceptualization: WY and SJ; Methodology: WY YH CB SJ and CY; Data Collection and analysis: WY YH CB and SJ; Writing: WY YH CB SJ and CY; Funding acquisition: SJ; Resources: SJ; Supervision: SJ.

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Correspondence to Sheng Jin.

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Appendices

Appendix A. The Impact of Different Trip Chain Breaking Time Thresholds on Trip Chain Breaking

In order to analyze the impact of different trip chain breaking time thresholds on trip chain breaking, the trip chain breaking time thresholds were set to 1604 s, 1904s, 2204 s, 2504 s, and 2804 s, respectively. Then, the average numbers of trips per vehicle per day under different trip chain breaking thresholds were calculated. The obtained results are shown in Fig. 12. According to Fig. 12, as the trip chain breaking time threshold increases, the average number of trips per vehicle per day gradually decreases. However, the change in the average number of trips is small with the change of trip chain breaking time thresholds. When the trip chain breaking time thresholds are 1604 s and 2804 s respectively, the difference in the average number of trips is only 0.25, indicating that the trip chain breaking results are relatively robust to the setting of the trip chain breaking time threshold.

Fig. 12
figure 12

Average number of trips per vehicle per day under different trip chain breaking time thresholds

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Appendix B. Statistical Test Results

2.1 Statistical Test Results on the Traffic Status of the Road Network

This paper analyzes whether significant differences exist among NVT and ATI of the road network between different stages by using one-way ANOVA. The results for all four stages showed that for NVT, F(3,24) = 343.88, p < 0.001 < 0.05, and for ATI, F(3,24) = 11.974, p < 0.001 < 0.05. At the 0.05 confidence level, the average value of NVT and ATI of all four stages were not completely equal, i.e., at least one stage is significantly different from the other stages. Therefore, the Tukey-Kramer test was used for pairwise comparison between stages, the results of which are shown in Table 1.

Table 1 Results of post hoc test
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2.2 Statistical Test Results of Non-Localized Operating Vehicles

One-way ANOVA was performed to analyze whether significant differences exist in NVT and ATI of non-localized operating vehicles in Yiwu city between the four stages. The results showed that for NVT, F(3,24) = 124.135, p < 0.001 < 0.05, which leads to the rejection of the null hypothesis. For ATI, F(3,24) = 6.585, p = 0.002 < 0.05, which also leads to the rejection of the null hypothesis. At the 0.05 confidence level, the average values of NVT and ATI of the four stages were not completely equal. Therefore, the Tukey-Kramer test was used for pairwise comparison between stages, the results of which are shown in Table 2.

Table 2 Results of post hoc test
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One-way ANOVA was performed to analyze Tavg and Davg of non-localized operating vehicles in the four stages. For Davg, F(3,24) = 4.398, p < 0.013 < 0.05, and for Tavg, F(3,24) = 266.482, p < 0.001 < 0.05, which leads to the rejection of the null hypothesis. Therefore, at the 0.05 confidence level, the average values of Tavg and Davg of the four stages were not completely equal. At least one stage was significantly different from other stages. Therefore, the Tukey-Kramer test was used for pairwise comparison between stages, the results of which are shown in Table 3.

Table 3 Results of post hoc test
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2.3 Statistical Test Results of Taxis

One-way ANOVA was performed to analyze whether significant differences exist in NVT and ATI of taxis in Yiwu city between the four stages. Because neither NVT nor ATI satisfy the hypothesis of homogeneity of variance, Welch ANOVA was adopted, and the NVT and ATI of taxis between different stages showed statistically significant differences. With regard to NVT, Welch F(3, 14.989) = 918.312, p < 0.001. With regard to ATI, Welch F(3, 14.447) = 104.684, p < 0.001. The Games-Howell test was used for pairwise comparison between stages, the results of which are shown in Table 4.

Table 4 Games-Howell test results
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One-way ANOVA was performed to analyze Tavg and Davg of taxis in the four stages. For the Davg of taxis, F(3,24) = 238.706, p < 0.001 < 0.05, and for the Tavg of taxis, F(3,24) = 1068.444, p < 0.001 < 0.05, which results in the rejection of the null hypothesis. Therefore, at the 0.05 confidence level, the average value of Tavg and Davg of taxis in the four stages were not completely equal. At least one stage exists that significantly differs from other stages. Therefore, the Tukey-Kramer test was used for pairwise comparison between stages, the results of which are shown in Table 5.

Table 5 Results of post hoc test
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2.4 Statistical Test Results of Localized Operating Vehicles

One-way ANOVA was performed to analyze whether significant differences exist in NVT and ATI of localized operating vehicles in Yiwu city between the four stages. With regard to NVT, since it does not satisfy the hypothesis of homogeneity of variance, Welch ANOVA was adopted (Welch F(3,9.441) = 117.766, P < 0.001 < 0.05), which resulted in the rejection of the null hypothesis. With regard to ATI, F(3,24) = 12.699, p < 0.001 < 0.05, which resulted in the rejection of the null hypothesis. Therefore, at the 0.05 confidence level, the average values of NVT and ATI of the localized operating vehicles in the four stages were not completely equal. At least one stage exists differs significantly from other stages. The Tukey-Kramer test was used for pairwise comparison between stages, the results of which are shown in Table 6.

Table 6 Tukey-Kramer test results
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One-way ANOVA was performed to analyze Tavg and Davg of localized operating vehicles in the four stages. For Davg of localized operating vehicles, F(3,24) = 32.497, p < 0.001 < 0.05, and for Tavg of localized operating vehicles, F(3,24) = 151.343, p < 0.001 < 0.05; thus, the null hypothesis was rejected. Therefore, at the 0.05 confidence level, the average value of Tavg and Davg of localized operating vehicles in the four stages were not completely equal. At least one stage exists that differs significantly from other stages. Therefore, the Tukey-Kramer test was used for pairwise comparison between stages, the results of which are shown in Table 7.

Table 7 Results of post hoc test
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Appendix C. Thermodynamic Maps of the Distribution of the Origin and Destination During Morning and Evening Peak Hours

Kernel density estimation was carried out for the analysis of the distribution of the origin and destination in the morning and evening peak hours at each stage. Thermodynamic maps are drawn to depict the results. Figure 13 shows the information of the travel pattern of localized operating vehicles from a spatial perspective in the morning peak in each stage. Figure 14 shows the information of the travel pattern of localized operating vehicles from a spatial perspective in the evening peak in each stage.

Fig. 13
figure 13

Information on the travel patterns of localized operating vehicles from a spatial perspective in the morning peak in different stages

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Fig. 14
figure 14

Information on the travel pattern of localized operating vehicles from a spatial perspective in the evening peak in different stages

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Yao, W., Hu, Y., Bai, C. et al. Exploring Impact of COVID-19 on Travel Behavior. Netw Spat Econ 24, 165–197 (2024). https://doi.org/10.1007/s11067-023-09610-2

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