Impact of COVID-19 pandemic on the people’s choice of urban public transportation modes and mobility in Addis Ababa and Hawassa city, Ethiopia

Figures & data

Figure 1. Public transportation network of Addis Ababa city mainly city-buses and minibus taxis.

Source: Adapted from Addis Ababa city structure and master plan (2017–2032).

Figure 2. Public transport network of Hawassa city.

Source: Adapted from (UN-Habitat, 2020).

Table 1. Operationalization of variables used to analyze the factors of mode choice.

Variables/Determinants of mode choice	Type
How high you place a priority on “total travel time savings” while choosing a mode?	Conventional
How high you place a priority on “travel cost affordability” while choosing a mode?	Conventional
How high you place a priority on “service availability” while choosing a mode?	Conventional
How high you place a priority on “accessibility of service” while choosing a mode?	Conventional
How high you place a priority on “comfort” while choosing a mode?	Conventional
How high you place a priority on “predictability of arrivals” while choosing a mode?	Conventional
How high you place a priority on “environment concern” while choosing a mode?	Conventional
How high you place a priority on “cleanliness” while choosing a mode?	Conventional
How high you place a priority on “COVID infection risk” while choosing a mode?	Pandemic-related
How high you place priority on “social distancing” while choosing a mode?	Pandemic-related
How high you place a priority on “Good air flow while traveling” when choosing?	Pandemic-related

Source: Adapted from Norman et al. (2005), Rogers (1975) and Rosenstock (1990).

Figure 3. Conceptual Framework: Causal Links between COVID-19 pandemic, PT Mode Choices and Travel Risks as Mediating Variables.

Source: Adapted from a protection motivation theory (Rogers, 1983; Norman et al., 2005).

Table 2. Perception of pandemic severity, travel risk vulnerability and protective measures.

S/N	Perception of pandemic severity, travel risk vulnerability and protective measures	Response (Frequency)
		Very high	High	Moderate	Low	Very low	Total
1	Size of new & total COVID positive cases every day in the area	292	338	23	11	6	670
2	Number of people recovered from COVID infection every day in the area	31	26	20	274	320	671
3	Pandemic-related death rates and numbers every day in the area	289	332	25	13	8	667
4	Vulnerability of travel risks while using PT, relative to pre-pandemic scenario	294	340	13	15	9	671
5	Implementation levels of intervention measures & safety strategies	37	32	19	268	313	669
6	Likelihood of continuing to choose PT as frequently as in the pre-pandemic period	101	n.a.	n.a.	n.a.	n.a.	669
7	Likelihood of reducing the choice of PT and shift to others, relative to pre-COVID period	499	n.a.	n.a.	n.a.	n.a.
8	Likelihood of avoiding travel or choosing any modes, relative to pre-COVID period	69	n.a.	n.a.	n.a.	n.a.

Very high = 5, High = 4, Moderate = 3, Low = 2, Very low = 1 and n.a.= not applicable.

Source: Field survey (2021).

Table 3. Mode choices of Passengers for daily trips before and during the pandemic.

Scenario	City	Modal Choice/share (Frequency)
		City-Bus	Midi-Bus	Mini-Bus	Bajaj	AA-LRT	Other	Total	Percent
Before COVID	Addis Ababa	104	63	81	25	75	n.a.	348	51.9
	Hawassa	58	20	111	134	0	n.a.	323	48.1
	Total	162	83	192	159	75	n.a.	671	100
During COVID	Addis Ababa	84	54	66	24	62	58	348	51.9
	Hawassa	49	16	105	129	0	24	323	48.1
	Total	133	70	171	153	62	82	671	100

Source: Field survey (2021).

Table 4. Factors determining PT mode choice before and during COVID-19 pandemic.

S/N	Wilcoxon Signed-Rank test on each factor/variable	Ranks		Test statistics
		Negative ranks	Positive ranks	Z	Sig. (2-tailed)
1	Travel time savings After COVID- Travel time savings before COVID	348	317	−10.452	0.05
2	Travel cost affordability After COVID- Travel cost affordability before COVID	101	562	−14.412	0.001
3	Service availability After COVID- Service availability before COVID	360	303	−0.2101	0.06
4	Accessibility of service After COVID- Accessibility of service before COVID	355	306	−0.0240	0.06
5	Comfort After COVID- Comfort before COVID	351	312	−0.0320	0.07
6	Predictability of arrivals After COVID- Predictability of arrivals before COVID	362	297	−0.1220	0.07
7	Cleanliness After COVID- Cleanliness before COVID	79	582	−12.153	0.001
8	Pandemic infection risk After COVID- Pandemic infection risk before COVID	71	592	−17.108	0.03
9	Social physical distancing After COVID- Social physical distancing before COVID	84	575	−16.108	0.001
10	Good airflow while traveling After COVID- Good airflow while traveling before COVID	87	570	−11.960	0.001

Source: Computed using survey data (2021).

Figure 4. Overall percentage share of public transport mode choice in both cities before and during the pandemic.

Source: Field survey (2021).

Figure 5. Total public transport choice frequency per week before and after COVID-19 in both cities.

Source: Computed using survey data (2021).

Table 5. Mode choice modeling during the COVID-19 period.

Independent variables	Parameter estimates
	Bajaj			Mini-bus
	B	Exp (B)	Sig.	B	Exp (B)	Sig.
Intercept	1.31	.657	0.000	−0.01	.365	0.004
Pandemic-related factor: infection risk fear	1.40	1.03	0.000	−0.01	1.46	0.004
Pandemic-related factor: cleanliness	0.90	0.723	0.021	0.12	0.615	0.011
Pandemic-related factor: social distancing	−0.12	0.24	0.072	−0.93	0.87	0.020
Sex-female	−0.70	0.54	0.036	0.42	1.32	0.000
Age 18–32 years	1.13	1.38	0.021	0.08	1.10	0.012
Age 33–47 years	0.40	.091	0.064	0.20	.791	0.106
Monthly Income −1	−0.15	0.43	0.000	−1.04	1.03	0.211
Monthly Income-2	0.24	0.09	0.013	0.50	0.86	0.000
Location distance-1	0.64	1.98	0.010	1.27	2.53	0.021
Location distance-3	0.63	0.98	0.100	−1.31	0.95	0.012
Trip cost-1	n.a.	n.a.	n.a.	−0.45	1.06	0.000
Trip cost-2	n.a.	n.a.	n.a.	−0.57	1.27	0.041

Source: Computed using survey data (2021).

Table 6. Probability of modal shift (from PT) in the future post-COVID (in 18–24 months).

S/N	Agreement/Disagreement level about the modal choice in the future (during & post-COVID period)	Response (Frequency)
		Mostly disagree	Disagree	Moderate	Agree	Mostly agree	Total
1	You are likely to use PT service again and again during the pandemic	192	222	83	99	75	671
2	You are likely to recommend PT service to friends, colleagues…	183	219	78	97	89	666
3	You are likely to shift (from PT) into the use of alternative modes of transport	78	99	85	218	189	669

Source: Computed using survey data (2021).

Table 7. The anticipated modal choice in the future or post-COVID (in 18–24 months).

The mode anticipated to be chosen in the future	Measures of the anticipated modal choice (%)					Total
	Much less	Some what less	About the same	Some what more	Much more
City-buses	22	32	21	19	6	201
Midi-buses	29	15	26	18	12	126
Mini-buses	9	30	36	14	11	398
Bajajs	10	13	26	20	31	256
Light rail	19	22	30	15	14	105
Walking	14	18	12	40	16	793
Bicycle	11	11	23	22	33	421
Auto/car use	33	18	13	21	15	41
None/wouldn’t make travel	10	13	16	25	36	539
Others	13	15	33	18	21	54
Total	n.a.	n.a.	n.a.	n.a.	n.a.	2934

The sum is beyond the total sample size because of the multiple responses of each respondent.

Source: Computed using survey data (2021).

Table 8. Justifications for the anticipated less use of and shift from public transport in the future.

Justification for why the shift from PT usage?	Frequency	Ranking
Greater exposure to and risk of COVID infections	376	1
Shortage of COVID infection prevention services on board	329	2
Unaffordable PT service fee	275	3
Out of necessity- disruption of service provision & availability	252	4
Commuters have been laid off	204	5
PT services become irregular and infrequent	187	6
Commuters have been working from home	146	7
By choice i.e. lower service quality	120	8
Commuters retirement	72	9
Inability to walk to/from stops or get on/off vehicles	69	10
Possibility of location change	63	11
PT services start too late and end too early	61	12
Possibility of using other modes (auto, bicycle, bike…)	47	13
Unwillingness to travel with strangers	45	14
PT services are not environmentally friendly	36	15
Others	26	16
Total	2308	n.a.

The sum is beyond the total sample size because of the multiple responses of each respondent.

Source: Computed using survey data (2021).

UN-Habitat. (2020). Sustainable Development of Hawassa City Cluster: Hawassa City Master Plan 2018-2020. Retrieved April 4, 2022, from http://ourcityplans.unhabitat.org/planning-experiences/sustainable-deveolopment-hawassa-city-cluster

 Google Scholar

Norman, P., Boer, H., & Seydel, E. R. (2005). Protection motivation theory. Predicting Health Behaviour, 81, 126. References - Scientific Research Publishing. (n.d.). https://www.scirp.org/(S(351jmbntvnsjt1aadkozje))/reference/referencespapers.aspx?referenceid=2906194

 Google Scholar

Rogers, R. W. (1975). A protection motivation theory of fear appeals and attitude change. The Journal of Psychology, 91(1), 93–114. PMID: 28136248. https://doi.org/10.1080/00223980.1975.9915803

 PubMed Web of Science ®Google Scholar

Rosenstock, I. M. (1990). The health belief model: Explaining health behavior through expectancies. In Glanz K, Lewis FM, Rimer BK (Eds.), Health behavior and health education (pp. 39–62). Jossey-Bass.

 Google Scholar

Rogers, R. W. (1983). Cognitive and physiological processes in fear appeals and attitude change: A revised theory of protection motivation. In J. Cacioppo & R. Petty (Eds.), Social Psychophysiology (pp. 153–177). Guilford Press.

 Google Scholar