Changes In Travel Behavior Research Articles

Malaria in Austria : Aretrospective analysis of malaria cases diagnosed at areference center in 2010-2020.

Although malaria is not endemic to Austria, each year infections are imported by travellers, migrants and refugees. This study aims to provide an overview of malaria cases diagnosed at an Austrian institute for tropical medicine between 2010 and 2020. Aretrospective, descriptive study was conducted based on the data of malaria cases confirmed at the Institute of Specific Prophylaxis and Tropical Medicine of the Medical University of Vienna. Laboratory diagnostics included microscopy, polymerase chain reaction (PCR) and real-time quantitative PCR. Overall, 122cases were identified. Annual case numbers were consistently higher from 2016 to 2020 than during the first half of the decade. Most malaria cases were diagnosed during summer and early autumn. This seasonal trend was not observed during the year 2020. With 55.1% (65/118) Plasmodium falciparum was the most common species, followed by Plasmodium vivax (19.5%, 23/118). The majority of patients were male (71.1%, 86/121) and the median age was 34.5 years (interquartile range, IQR 22.5-47.0 years). With amedian age of 20.0 years (IQR 14.0-32.0 years), patients with P.vivax infections were younger than those infected with other Plasmodium species. Moreover, they were mostly male (82.6%, 19/23). From 2010 to 2020, the number of malaria cases diagnosed at the center increased. Growing international mobility and changing travel behavior could at least partly be responsible for this trend and there are indications that particularly P.vivax infections were imported by migrants and refugees.

Impact of Life Events on Incremental Travel Behavior Change

Travel demand models regenerate the travel behavior of persons or households from scratch at every model run. However, the literature suggests that travel behavior remains relatively stable over time. Change in travel behavior is triggered by life events such as change in employment, household relocation, or birth of a child. The inability of existing travel demand models to represent habitual travel behavior and change in travel behavior of a person/household becuase of life events tends to exaggerate policy sensitivity and result in longer model run times to recreate travel behavior for every agent. In this study, we examined the travel behavior of persons between two consecutive years using a mobility panel survey from Germany. The travel behavior of persons with and without a life event is compared econometrically. Here, the travel behavior is measured as the number of weekly trips by activity type and mode and the impacts of six types of life events are studied. The results show that life events affect travel behavior, but the degree of impact varies by the type of life event, the trip purpose, and the mode. In some cases the impact is found to be negligible, but for many other cases the impact is profound. Moreover, general trends (not affected by life event) in travel behavior are also found. It is concluded that such dynamics in travel behavior should be represented by travel demand models for more sensible policy testing and computationally efficient travel demand models.

Change in departure time for a train trip to avoid crowding during the COVID-19 pandemic: A latent class study in the Netherlands.

After the outbreak of COVID-19 pandemic, crowding has been highlighted as a risk factor for contracting acute respiratory infections (ARIs) such as COVID-19, which has affected the demand for public transport. Although several countries, including the Netherlands, have implemented differential fare systems for peak and off-peak travel to reduce crowding during the rush hours, the problem of overcrowding on trains has remained prevalent and is expected to cause more disutility than even before the pandemic. A stated choice experiment in the Netherlands is conducted to understand the extent to which people can be motivated to change their departure time to avoid crowded trains during rush hours by offering them real-time information on on-board crowding levels and a discount on the train fare. To gain further insights into how travelers respond to crowding and capture unobserved heterogeneity in the data, latent class models have been estimated. Unlike the previous studies, the respondents were segregated into two groups before the start of the choice experiment based on their indicated preference to schedule a delay earlier or later than their desired departure. To study the change in travel behavior during the pandemic, the context of different vaccination stages was also provided in the choice experiment. Background information collected in the experiment was broadly categorized as socio-demographic, travel and work-related factors, and attitudes towards health and COVID-19. It was found that the coefficients obtained for the main attributes which were presented in the choice experiment (on-board crowd levels, scheduled delay and discount offered on full fare) were found statistically significant, and in line with previous research. It was concluded that when most of the people are vaccinated in the Netherlands, the travelers become less averse to on-board crowding. The research also indicates that certain groups of respondents, such as those who are highly crowd averse, and are not students, can be motivated to change their departure time if real-time crowding information was provided. Other groups of respondents who were found to value fare discounts can also be motivated to change their departure by similar incentives.

Methodology to Add Value to Ageing Travel Survey Data

Over past the several decades, most transportation organizations have gathered a large amount of data through travel surveys. The data they provide is typically used as the basis of their transportation planning and modeling processes. In the province of Quebec, regional household travel surveys take place every 5 to 10 years in the main regions. Conducting large-scale surveys more frequently is not always possible, especially for less populous areas. Relying on old data is, however, becoming increasingly problematic. In this context, it is interesting to explore methods to value historical travel surveys in innovative ways. Based on two travel surveys from the city of Sherbrooke in Quebec, a large-scale regional survey from 2012 and a smaller ad-hoc travel survey from 2019, this paper proposes a methodology for combining the two survey samples. The objective of this process is to benefit from the advantages of both samples: (i) up-to-date travel behaviors from the 2019 survey and (ii) a large and controlled sample size from the 2012 survey. The integration process relies on proportional iterative updating. The descriptive analysis of the two surveys confirmed significant changes in travel behaviors and population had occurred between 2012 and 2019. Despite these differences, the results obtained with the combined samples allow us to reproduce faithfully the trip behaviors and populations of 2019. The proposed method therefore confirms a strong potential to gain better value from historical travel survey data using innovative data combination approaches.

Evaluation of the impact of Covid-19 on transport sustainability in Iran

The Covid-19 outbreak changed travel behaviour in many ways. This paper evaluates these changes in Tehran, Iran, from a transportation sustainability perspective. It uses data from travel surveys before and during the pandemic to evaluate changes in travel activity and their impacts on public transportation (PT) system costs and revenues, air quality and traffic crashes. A structural equation model (SEM) is used to assess the potential impact of passengers’ characteristics, details of each transportation mode and the severity of Covid-19 on travel behaviour. Hypothesis testing is used to compare the changes that occurred in air quality and traffic crashes. SEM outputs indicate that the frequency of trips previously made by sustainable modes and changes in regular trips have the highest impact on the sustainability of trips made during the Covid-19 pandemic. Air quality declined in many city districts. The pandemic caused reductions in PT ridership and an increase in private car use, which threaten the long-term sustainability of public transit services. Crash fatalities declined slightly, particularly during periods of movement restrictions. However, motorcycle fatal crashes increased in comparison with the reduction in motorcycle trips, apparently due to increased traffic speeds. The government did not use the pandemic as an opportunity to promote sustainable and low-contagion modes. This analysis infers that the Covid-19 outbreak reduced the overall transportation sustainability in Tehran.

Post-pandemic shared mobility and active travel in Alabama: A machine learning analysis of COVID-19 survey data

The COVID-19 pandemic has had unprecedented impacts on the way we get around, which has increased the need for physical and social distancing while traveling. Shared mobility, as an emerging travel mode that allows travelers to share vehicles or rides has been confronted with social distancing measures during the pandemic. On the contrary, the interest in active travel (e.g., walking and cycling) has been renewed in the context of pandemic-driven social distancing. Although extensive efforts have been made to show the changes in travel behavior during the pandemic, people’s post-pandemic attitudes toward shared mobility and active travel are under-explored. This study examined Alabamians’ post-pandemic travel preferences regarding shared mobility and active travel. An online survey was conducted among residents in the State of Alabama to collect Alabamians’ perspectives on post-pandemic travel behavior changes, e.g., whether they will avoid ride-hailing services and walk or cycle more after the pandemic. Machine learning algorithms were used to model the survey data (N = 481) to identify the contributing factors of post-pandemic travel preferences. To reduce the bias of any single model, this study explored multiple machine learning methods, including Random Forest, Adaptive Boosting, Support Vector Machine, K-Nearest Neighbors, and Artificial Neural Network. Marginal effects of variables from multiple models were combined to show the quantified relationships between contributing factors and future travel intentions due to the pandemic. Modeling results showed that the interest in shared mobility would decrease among people whose one-way commuting time by driving is 30–45 min. The interest in shared mobility would increase for households with an annual income of $100,000 or more and people who reduced their commuting trips by over 50% during the pandemic. In terms of active travel, people who want to work from home more seemed to be interested in increasing active travel. This study provides an understanding of future travel preferences among Alabamians due to COVID-19. The information can be incorporated into local transportation plans that consider the impacts of the pandemic on future travel intentions.

Quantifying the impact of COVID-19 on travel behavior in different socio-economic segments

The COVID-19 pandemic has resulted in substantial negative impacts on social equity. To investigate transport inequities in communities with varying medical resources and COVID controlling measures during the COVID pandemic and to develop transport-related policies for the post-COVID-19 world, it is necessary to evaluate how the pandemic has affected travel behavior patterns in different socio-economic segments (SES). We first analyze the travel behavior change percentage due to COVID, e.g., increased working from home (WFH), decreased in-person shopping trips, decreased public transit trips, and canceled overnight trips of individuals with varying age, gender, education levels, and household income, based on the most recent US Household Pulse Survey census data during Aug 2020 ∼ Dec 2021. We then quantify the impact of COVID-19 on travel behavior of different socio-economic segments, using integrated mobile device location data in the USA over the period 1 Jan 2020–20 Apr 2021. Fixed-effect panel regression models are proposed to statistically estimate the impact of COVID monitoring measures and medical resources on travel behavior such as nonwork/work trips, travel miles, out-of-state trips, and the incidence of WFH for low SES and high SES. We find that as exposure to COVID increases, the number of trips, traveling miles, and overnight trips started to bounce back to pre-COVID levels, while the incidence of WFH remained relatively stable and did not tend to return to pre-COVID level. We find that the increase in new COVID cases has a significant impact on the number of work trips in the low SES but has little impact on the number of work trips in the high SES. We find that the fewer medical resources there are, the fewer mobility behavior changes that individuals in the low SES will undertake. The findings have implications for understanding the heterogeneous mobility response of individuals in different SES to various COVID waves and thus provide insights into the equitable transport governance and resiliency of the transport system in the “post-COVID” era.

Investigating heterogeneity in travel behaviour change when implementing soft transport interventions: A latent class choice model

AbstractAttracting more travellers to shift towards green modes plays a significant role in sustainable transportation development. Soft transport interventions are important strategies for facilitating voluntary travel behaviour change. This study investigates the effects of two soft transport interventions, information intervention and public transport service improvement, on heterogeneous traveller groups’ behaviour change. Beijing, China was selected as the case study site. Firstly, three heterogenous traveller groups were identified based on latent class cluster analysis: Group A (20.4%, travel in low‐frequency and prefer multimode), Group B (30.3%, travel in middle‐frequency and prefer the car) and Group C (49.3%, travel in high frequency and prefer green modes). Then, latent class choice models under two transport interventions were established respectively. The findings indicate that information intervention decreases Group B's preference for car use and exclusion of bicycles. Little effect is observed on travel behaviour change of Group A and C. Public transport service improvement significantly reduces car use of Group B and C. This study demonstrates a limited influence of information intervention on travel behaviour change and public transport service improvement is effective in achieving car use reduction. The results provide decision makers with the information needed to target interventions for different traveller groups.

Exploring the potential role of bikeshare to complement public transit: The case of San Francisco amid the coronavirus crisis

The recent worldwide SARS-CoV-2 (COVID-19) pandemic has reshaped the way people live, how they access goods and services, and how they perform various activities. For public transit, there have been health concerns over the potential spread to transit users and transit service staff, which prompted transportation agencies to make decisions about the service, e.g., whether to reduce or temporarily shut down services. These decisions had substantial negative consequences, especially for transit-dependent travelers, and prompted transit users to explore alternative transportation modes, e.g., bikeshare. However, local governments and the public in general have limited information about whether and to what extent bikeshare provides adequate accessibility and mobility to those transit-dependent residents. To fill this gap, this study implemented spatial and visual analytics to identify how micro-mobility in the form of bikesharing has addressed travel needs and improved the resilience of transportation systems. The study analyzed the case of San Francisco in California, USA, focusing on three phases of the pandemic, i.e., initial confirmed cases, shelter-in-place, and initial changes in transit service. First, the authors implemented unsupervised machine learning clustering methods to identify different bikesharing trip types. Moreover, through spatiotemporally matching bikeshare ridership data with transit service information (i.e., General Transit Feed Specification, GTFS) using the tool called OpenTripPlanner (OTP), the authors studied the travel behavior changes (e.g., the proportion of bikeshare trips that could be finished by transit) for different bikeshare trip types over the three specified phases. This study revealed that during the pandemic, more casual users joined bikeshare programs; the proportion of recreation-related bikeshare trips increased; and routine trips became more prevalent considering that docking-station-based bikeshare trips increased. More importantly, the analyses also provided insights about mode substitution, because the analyses identified an increase in dockless bikeshare trips in areas with no or limited transit coverage.

Effectiveness of travel behavior and infrastructure change to mitigate heat exposure

Urban heat exposure is an increasing health risk among urban dwellers. Many cities are considering accommodating active mobility, especially walking and biking, to reduce greenhouse gas emissions. However, promoting active mobility without proper planning and transportation infrastructure to combat extreme heat exposure may cause more heat-related morbidity and mortality, particularly in future with projected climate change. This study estimated the effectiveness of active trip heat exposure mitigation under built environment and travel behavior change. Simulations of the Phoenix metro region's 624,987 active trips were conducted using the activity-based travel model (ABM), mean radiant temperature (TMRT, net human radiation exposure), transportation network, and local climate zones. Two scenarios were designed to reduce traveler exposure: one that focuses on built environment change (making neighborhoods cooler) and the other on travel behavior (switching from shorter travel time but higher exposure routes to longer travel time but cooler routes) change. Travelers experienced TMRT heat exposure ranging from 29°C to 76°C (84°F to 168°F) without environmental or behavioral change. Active trip TMRT exposures were reduced by an average of 1.2–3.7°C when the built environment was changed from a hotter to cooler design. Behavioral changes cooled up to 10 times more trips than changes in built environment changes. The marginal benefit of cooling decreased as the number of cooled corridors transformed increased. When the most traveled 10 km of corridors were cooled, the marginal benefit affected over 1,000 trips/km. However, cooling all corridors results in marginal benefits as low as 1 trip/km. The results reveal that heavily traveled corridors should be prioritized with limited resources, and the best cooling results come from environment and travel behavior change together. The results show how to surgically invest in travel behavior and built environment change to most effectively protect active travelers.

How mentally healthy we are during the pandemic? Findings from changed travel behavior

BackgroundMany people changed their travel behavior during the coronavirus pandemic with more telecommuting, fewer trip frequencies, and less use of transit and ride-hailing to avoid infection. The lack of outdoor activities may result in social isolation and then trigger anxiety or depressive symptoms. Research objectiveThis study examines the relationship between anxiety and depression, and correlates various sociodemographic, income, job status, health-related factors, and travel behavior changes in six large U.S. cities. DataU.S. Census Household Pulse Survey Phases 3.0 and 3.1 are employed. MethodGAD-2 and PHQ-2 are used to screen the scores of anxiety and depression. The synthetic minority oversampling technique is applied to correct sample distribution. The multivariate mixed model is employed to examine relationships. Results(1) Anxiety and depression are positively correlated, and the percentage of high anxiety is greater than the percentage of high depression. (2) The levels of anxiety and depression significantly vary across the six cities. (3) Women, young, singles, and white people have higher levels of anxiety and depression during the pandemic. (4) People who are willing to receive vaccination tend to have higher levels of anxiety and depression. (5) The prevalence of depressive disorders is significantly lower in the high-income group. (6) People who applied for unemployment insurance and experienced expense difficulties are more likely to suffer high levels of anxiety and depression. (7) Travel behavior changes, measured by increased telecommuting, reduced trip frequency, and reduced use of transit and ride-hailing, all suggest positive correlations with anxiety and depression. ConclusionsMore assistance and attention should be given to women, singles, and low-income households to reduce the prevalence of mental stress in vulnerable groups. Telecommuting can be but need to work with other travel demand management strategies. Travel and outdoor activities should be promoted under the new normal.

Understanding Travel Behavior Change during COVID-19 Using Spatio-temporal Cluster Analysis

Understanding Travel Behavior Change during COVID-19 Using Spatio-temporal Cluster Analysis

Travel behaviour changes and risk perception during COVID-19: A case study of Malaysia

The COVID-19 pandemic has disrupted travel behaviours due to the need for movement restrictions. The restrictions adversely affected various aspects of health and the economy. This study aimed to investigate factors affecting trip frequency during the recovery period of the COVID-19 pandemic in Malaysia. An online national cross-sectional survey was conducted to collect data in conjunction with different movement restriction policies. The questionnaire includes socio-demographics, experience with COVID-19, risk perception of COVID-19, and trip frequency on several activities during the pandemic. Mann Whitney U was conducted to determine whether there were statistically significant differences between the socio-demographic factors for the respondents in the first and second surveys. Results show no significant difference in socio-demographic factors except for the level of education. The results indicate that the respondents from both surveys were comparable. Next, Spearman correlation analyses were conducted to find significant correlations between trip frequencies toward socio-demographics, experience with COVID-19 and risk perception. There was a correlation between the frequency of travel and risk perception for both surveys. Regression analyses were performed based on the findings to investigate trip frequency determinants during the pandemic. Perceived risk, gender, and occupation influenced the trip frequencies for both surveys. By understanding the influence of risk perception on the frequency of travel, the government can identify the appropriate policy during a pandemic or health emergency to avoid impeding normal travel behaviour. Thus, people's mental and psychological well-being are not negatively affected.

Study of COVID-19 impact on users' perception for transport modes choice in India

The COVID-19 pandemic has adversely affected the transportation sector, leading to a change in travel behavior. One of the most important responses of countries worldwide to slow the spread of the pandemic has been to restrict the movement of people, which has had a considerable effect on transport systems and is likely to affect the modal choices of commuters. The social and economic effects of the COVID-19 epidemic on public transportation encompass beyond service performance and health risks to social equity, financial viability, and sustainable mobility. To assess the likely nature of such a shift, this study attempts to understand mode choice based on the perception of commuters for their work and non-work trips after the lockdown phase due to COVID-19. The Multinomial Logit Model was used in this study to find the association of mode choice with health-related safety perceptions of commuters for private and public transport, and their socio-economic and travel characteristics. Mode choice for private transport was found for work trips, whereas non-motorized transport for non-work (shopping, leisure, etc.) trips up to a distance of 10 km. The model results showed that significant factors for the increase in the usage of private transport and non-motorized transport relative to public transport were physical distancing and hygiene. This study provides results for future transportation policies in the post-COVID period. The results, like emerging positive perceptions of non-motorized modes, are very useful for making better investments in the country to promote sustainable transportation and articulating solutions to address high-risk perceptions associated with public transport modes.

Investigating Anticipated Changes in Post-Pandemic Travel Behavior: Latent Segmentation-Based Logit Modeling Approach Using Data From COVID-19 Era

The unprecedented situation created by the COVID-19 pandemic in the year 2020 has drastically changed daily mobility patterns around the world. Various measures were implemented to prevent the transmission of the virus, which have resulted in short- and long-term impacts on the activity systems and daily travel. To capture the impacts of the pandemic on travel behaviors and activity systems, a web-based survey was designed and administered in April–May 2020 in Montreal, Canada. In addition to questioning on pre- and during COVID-19 behaviors, it included a section on how people expected to travel, telework, shop online, and so forth in the post-pandemic era. Using data from this survey, which gathered 1,620 completed questionnaires, this paper proposes insights into how people are planning to travel in a post-COVID-19 world using latent segmentation-based logit modeling technique. Three models are estimated to identify factors related to expected trip frequency, expected transit usage, and expected bike usage. Undertaking such modeling approach provides opportunity to understand different types of individuals’ preferential behaviors. This study probabilistically identifies two latent segments, suburbanite and urbanite people, and finds considerable heterogeneity across sample individuals. For example, urbanite people tend to increase their expected number of trips after COVID-19 if they have at least one bike in their household. Suburbanite people exhibit an opposite relationship, and they are more likely to keep their trip frequency the same as before. Findings of this study will assist decision makers in developing effective policy measures to better prepare for the changes in travel behaviors after COVID-19.

Gender differentials in travel behavior among TOD neighborhoods: Contributions of built environment and residential self-selection

Transit-Oriented Development (TOD) has become essential for rapidly expanding cities in reducing vehicle kilometers traveled (VKT) and increasing public transport usage in their neighborhoods. Numerous studies explored the effects of built environment and residential self-selection on the travel behavior of individuals living in TOD neighborhoods. However, few studies quantified relative contributions of these combined effects to travel behavior changes. Of them, gender-disaggregated studies are very less, especially from developing nations. The present study fills this gap by quantifying the relative contributions of built environment and residential self-selection on travel behavior outcomes such as VKT, public trips, active trips, and private trips of 3349 male and 2298 female individuals living in four TOD types of Delhi, India. For this purpose, this study has employed hierarchical clustering method for segmenting 48 neighborhoods into TOD typology, and propensity score matching method for controlling residential selection among TOD type pairs. Propensity score matching of male and female individuals among TOD pairs indicated that most individuals choose to live in balanced-TODs. After controlling for residential self-selection, female individuals in balanced-TODs tend to make shorter (98% reduction), and fewer (54% reduction) private trips, and higher (63% increase) active trips as compared to females in residential-TODs. For females, its built environment matters most, but for males, residential self-selection has an equal effect as built environment on travel behavior. Findings from this study are useful for planners and policymakers attempting gender-based policies in upcoming TOD cities.

Understanding individual-level travel behavior changes due to COVID-19: Trip frequency, trip regularity, and trip distance

Understanding different mechanisms in trip changes depending on transportation modes due to COVID-19 pandemic is the key to providing practical insights for healthy communities. This study aimed to investigate the impact of the COVID-19 pandemic on individual-level travel behavior in Daejeon Metropolitan City, South Korea. Using smart card and private vehicle records, we explored different travel behaviors exhibited while using buses and private vehicles. An individual's travel behavior was represented in trip frequency, trip regularity, and trip distance and was compared weekly for about three months, including the initial period of pandemic. A significant decrease in trip frequency during non-peak hours on weekdays and during weekends indicates that people reduced non-mandatory trips more than commuter trips. This was also verified in that, as the number of infection cases increased, trip regularity with 24-hour intervals intensified. People maintained the size of their activity boundaries but reduced their daily trip distances. The interesting point is that private vehicle usage increased for shorter trip distances while bus usage dropped regardless of the ranges of trip distances under the pandemic. The findings provide evidence of possible inequality issues in transportation during the pandemic and can help make precautionary policies for future pandemics.

How Has COVID-19 Changed the Way We Travel? Exploring Tourist Personality, Reactions to the Perceived Risk and Change in Travel Behavior

The COVID-19 pandemic created novel conditions for researching travel behavior and tourists’ reactions in times of crisis, which largely differs from previous studies of travel behavior affected by local risks or lower travel and recreational risks. This study aims to provide an understanding of the relationship between tourist personality (MINI IPIP-6 and sensation seeking), tourists’ reactions to travel risk perception and changes in their travel behavior influenced by the COVID-19 pandemic. To explore this, a global survey including 905 respondents from four countries (Spain, Croatia, Serbia and Russia) was conducted, while data were analyzed by Structural Equation Modeling (SEM) in AMOS. The findings suggest that tourist personality affects the changes in travel behavior influenced by COVID-19, both directly and via their reactions to travel risk.

Investigating influencing factors for ICT adoption that changes travel behavior in response to the COVID-19 outbreak in Indonesia

Mobility and out-of-home activities restrictions from the COVID-19 pandemic have forced people to maximize their in-home activities. Considering the increase in Information and Communication Technology (ICT) adoption during the outbreak, this paper tries to shed light on the factors that influence changing travel behavior. For these purposes, the study uses data collected through an online questionnaire during the outbreak in Indonesia, which was then analyzed using cluster and discriminant analyses. The study found that online adaptation during the outbreak was affected by income level, whereby high-income individuals are associated with high online adaptation. Residential location also influences ICT adaptation during the outbreak. Cities with higher access to the internet and ICT platforms as well as higher income per capita tend to have higher ICT adoption. People with more experience with online platforms or services also record higher online in-home activity adoption during the outbreak. Furthermore, while the lower-income group tends to reduce their travel, the higher-income group still continues to travel to fulfil their household needs during the outbreak, such as in-store shopping. Since the lower-income group and less accessible areas tend to have difficulty in accessing ICT as a substitute for travel, this study recommends that the government and ICT stakeholders ensure equal access to ICT to support physical distancing and to limit mobility in order to flatten the peak of the pandemic.

What is the potential of hybrid cordon pricing policy for reducing and shifting car usage in cities?

As cities need to urgently transition out of unsustainable mobility systems, following the Avoid-Shift-Improve framework, changes in travel behavior have a top priority. Although behavior change in is a complex phenomenon, previous research informs us about the importance of urban road-usage policy measures. One such policy measure is often referred to as congestion pricing (CP). Some cities have successfully implemented CP, showing its effectiveness potential in behavior change, but wide-spread implementation lags due to challenges in societal, organizational, and political acceptance and implementability. Emerging literature on mobility credits (MC), mostly based on experiments, has brought forward the questions of acceptance, showing different possibilities for credit allocation, accumulation and exchange. In addition to behavior change, cities in transition economies might also consider monetary policies as potential solutions to financing issues. This research aims to analyze benefits and challenges of implementing a hybrid scheme that is a combination of CP and MC, developed with an expectation for policy coherence and complementarity. To determine the effects of the proposed hybrid scheme, a comparative analysis of user attitudes on congestion charging was conducted. The research was conducted using an online questionnaire, and the target group consisted of the residents of Belgrade (Serbia). Since these strategies are not implemented in Belgrade, the stated choice approach was used. The survey contained questions about the socio-demographic characteristics of the users, trip characteristics, users’ acceptability of CP and the hybrid scheme, as well as the questions on transport mode usage for commuting trips and for trips with other purposes. Based on statistical and policy analysis, it was determined that the hybrid scheme represents a suitable compromise solution which would reduce passenger car trips in the city center and while increase public acceptance for such normative policies.