Logit Mode Choice Model Research Articles

Empowering revealed preference survey with a supplementary stated preference survey: demonstration of willingness-to-pay estimation within a mode choice case

Mode choice models play a pivotal role in transport demand modelling and help transport planners, engineers and researchers with policy and infrastructure investment evaluation. Recent mode choice studies primarily use revealed preference (RP) data to reflect individuals’ true behaviour. However, this may not be the best practice, given the lack of information in RP data. This study uses a nonlinear utility specification for a multinomial logit mode choice model development using high-quality travel data collected by a GPS-based smartphone application complemented by stated preference (SP) data. The model results highlight the impact of sociodemographic variables on mode choice behaviour and individuals’ willingness-to-pay (WTP) when the model is jointly developed compared to stand-alone SP and RP models. The main message of this study is that in addition to collecting RP, which is a reliable and unbiased source of data, collecting complementary SP data is beneficial as it provides information that is not otherwise available in RP data. This may include a proper variation in the public transport cost variable as demonstrated in this study. Moreover, to better understand the travellers' behaviour regarding the trade-off between time and cost a mixed multinomial logit (MMNL) model in the willingness to pay space is developed on the SP data. capturing the unobserved heterogeneity within the estimated WTPs, the MMNL model outputs reveal a higher variation in WTP of car in-vehicle travel time compared to bus in-vehicle travel time.

Value of travel time savings of autonomous vehicle commuters: a segmented valuation for local and inter-city travel

ABSTRACT In this study we aim to identify how the adoption of autonomous vehicles (AVs) affects the value of travel time savings (VOTTS) for commuting trips under different trip distance scenarios (local and inter-city travel). Using a stated preference survey administered to Korean commuters in 2019, two multinomial logit (MNL) mode choice models are developed. These models are stratified by trip length and include four alternatives: AV with a manual driving option (AVMD), AV with a self-driving option (AVSD), shared AV (SAV), and public transit (PT). The results show that the value of in-vehicle travel time savings for AVSD (on average, $7.61/hr) is lower than for the other two AV modes (AVMD = $10.26/hr, SAV = $13.67/hr). It suggests that travelers tend to pay less to reduce travel time while using a private hands-free mode because it allows travelers to use their travel time more productively and/or relieve stress for driving. In addition, changes in the VOTTS for each mode vary by trip length. As travel distance increases, VOTTS for shared modes (SAV and PT) tends to decrease. The value of in-vehicle travel time for SAV in the local travel scenario is the highest among the four modes but is considerably lower than that in the inter-city scenario. These differences suggest that travelers require a certain amount of time to fully use their in-vehicle time, highlighting the importance of trip length when determining the VOTTS. Interestingly, the VOTTS for PT is lower than three AV modes, implying that it might be too early to expect substantial reduction in the VOTTS for AVs due to potential risks of accidents, unfamiliarity with AVs, or immature technology of AVs. Highlights Likelihood of choosing autonomous vehicles is modelled based on trip length. Changes in value of travel time savings for each mode vary by trip length. Value of travel time savings for public transit is lower than autonomous vehicles. Self-driving vehicles have a low value of travel time savings than other modes. People need a certain amount of time to fully perform activities while traveling.

Deriving transport appraisal values from emerging revealed preference data

Transport demand models are widely used to inform policy making and produce forecasts of future demand. A core output derived from demand models is the Value of Travel Time (VTT), which provides insights on the trade-offs that travellers are willing to make in terms of travel time and travel cost. VTT estimates are a critical input to cost-benefit analyses and feasibility assessments of potential projects and thus play a crucial role in transport planning and policy decisions. While much of the early work on VTT made use of revealed preference (RP) data, their use decreased due to growing concerns about reporting errors that may result in omitted observations and measurement errors in the model inputs. As a consequence, VTT measures have, for the last two decades, primarily been estimated using state-preference (SP) surveys. While SP methods can assess the individual trade-offs in a controlled manner, they are prone to behavioural incongruence. More recently, RP data from passively-collected data sources have raised the promise of accounting for some of the limitations of traditional RP surveys due to the minimal (or even no) active input from the respondent. The present study utilises such a dataset that combined a 2-week trip diary captured through smartphone GPS tracking with a household survey containing individual socio-demographic information. A mixed Logit model for mode choice was specified and the estimated parameters were then applied on the National Travel Survey to calculate the VTT estimates. Those estimates were further adjusted based on trip distances to get more representative national VTT values. This process resulted in estimates similar to the official UK guidelines used in transport appraisal that were obtained from SP data, where our results are not affected by concerns about response quality or survey artefacts. The findings hence strengthen the case for shifting towards passively generated RP data sources and are important for transport practitioners.

Private Autonomous Vehicles and Their Impacts on Near-Activity Location Travel Patterns: Integrated Mode Choice and Parking Assignment Model

The goal of this study was to analyze the impact of private autonomous vehicles (PAVs), specifically their near-activity location travel patterns, on vehicle miles traveled (VMT). The study proposes an integrated mode choice and simulation-based parking assignment model, along with an iterative solution approach, to analyze the impacts of PAVs on VMT, mode choice, parking lot usage, and other system performance measures. The dynamic simulation-based parking assignment model determines the parking location choice of each traveler as a function of the spatial–temporal demand for parking from the mode choice model, whereas the multinomial logit mode choice model determines mode splits based on the costs and service quality of each travel mode coming, in part, from the parking assignment model. The paper presents a case study to illustrate the power of the modeling framework. The case study varies the percentage of persons with a private vehicle (PV) who own a PAV versus a private conventional vehicle (PCV). The results indicated that PAV owners traveled an extra 0.11 to 1.51 mi compared with PCV owners on average, and the PV mode share was significantly higher for PAV owners. Therefore, as PCVs are converted into PAVs in the future, the results indicate substantial increases in VMT near activity destinations. However, the results also indicated that adjusting parking fees and redistributing parking lot capacities could reduce VMT. The significant increase in VMT from PAVs implies that planners should develop policies to reduce PAV deadheading miles near activity locations, as the automated era comes closer.

Exploring Travel Mode Preference of External Trips for Smart City Transportation Planning: Sejong, Korea

In the face of growing concerns about urban problems, smart cities have emerged as a promising solution to address the challenges, for future sustainable societies in cities. Since the early 2000s, 67 local governments in Korea have been participating in smart city projects, as of 2019. The Sejong 5-1 Living Area smart city was selected as one of two pilot national demonstration smart cities. The main objectives of this study are to introduce the Sejong 5-1 Living Area smart city project that is currently in the planning stage, present travel and mode preferences focusing on external trips in a smart city context to be built, and analyze a mode choice model according to the socioeconomic characteristics of individual travelers. One of the distinguishing features of the Sejong smart city is its transportation design concept of designating a sharing car-only district within the city to limit private vehicle ownership to about one-third of residents, while bus rapid transit (BRT) plays a central role in mobility for external trips among four transport modes including private cars, BRT, carsharing, and ridesharing. This study was analyzed using the stated preference survey data under hypothetical conditions by reflecting the unique characteristics of the Sejong smart city transportation policy. Approximately two-thirds of respondents in the survey preferred to spend less than 1.25 USD, traveling less than 35 min on BRT trips. On the basis of the survey data, we developed a mixed logit mode choice model and found the overall model estimates to be statistically significant and reasonable. All people-specific variables examined in this study were associated with mode choices for external commuting trips, including age, income, household size, major mode, driving ability, and presence of preschoolers.

A freight origin-destination synthesis model with mode choice

This paper develops a novel procedure to conduct a Freight Origin-Destination Synthesis (FODS) that jointly estimates the trip distribution, mode choice, and the empty trips by truck and rail that provide the best match to the observed freight traffic counts. Four models are integrated: (1) a gravity model for trip distribution, (2) a binary logit model for mode choice, (3) a Noortman and Van Es’ model for truck, and (4) a Noortman and Van Es’ model for rail empty trips. The estimation process entails an iterative minimization of a nonconvex objective function, the summation of squared errors of the estimated truck and rail traffic counts with respect to the five model parameters. Of the two methods tested to address the nonconvexity, an interior point method with a set of random starting points (Multi-Start algorithm) outperformed the Ordinary Least Squared (OLS) inference technique. The potential of this methodology is examined using a hypothetical example of developing a nationwide freight demand model for Bangladesh. This research improves the existing FODS techniques that use readily available secondary data such as traffic counts and link costs, allowing transportation planners to evaluate policy outcomes without needing expensive freight data collection. This paper presents the results, model validation, limitations, and future scope for improvements.

More than Bike Lanes—A Multifactorial Index of Urban Bikeability

The present study aims to deduce bikeability based on a collective understanding and provides a methodology to operationalize its calculation based on open data. The approach contains four steps building on each other and combines qualitative and quantitative methods. The first three steps include the definition and operationalization of the index. First, findings from the literature are condensed to determine relevant categories influencing bikeability. Second, an expert survey is conducted to estimate the importance of these categories to gain a common understanding of bikeability and merge the impacting factors. Third, the defined categories are calculated based on OpenStreetMap data and combined to a comprehensive spatial bikeability index in an automated workflow. The fourth step evaluates the proposed index using a multinomial logit mode choice model to derive the effects of bikeability on travel behavior. The expert process shows a stable interaction between the components defining bikeability, linking specific spatial characteristics of bikeability and associated components. Applied components are, in order of importance, biking facilities along main streets, street connectivity, the prevalence of neighborhood streets, green pathways and other cycle facilities, such as rental and repair facilities. The mode choice model shows a strong positive effect of a high bikeability along the route on choosing the bike as the preferred mode. This confirms that the bike friendliness on a route surrounding has a significant impact on the mode choice. Using universal open data and applying stable weighting in an automated workflow renders the approach of assessing urban bike-friendliness fully transferable and the results comparable. It, therefore, lays the foundation for various large-scale cross-sectional analyses.

The influence of panel effects and inertia on travel cost elasticities for car use and public transport

Studies on the impact of changes in travel costs on car and public transport use are typically based on cross-sectional travel survey data or time series analysis and do not capture intrapersonal variation in travel patterns, which can result in biased cost elasticities. This paper examines the influence of panel effects and inertia in travel behaviour on travel cost sensitiveness, based on four waves of the Mobility Panel for the Netherlands (comprising around 90,000 trips). This paper analyses the monetary costs of travel. Panel effects reflect (within wave) intrapersonal variations in mode choice, based on three-day trip diary data available for each wave. The impact of intrapersonal variation on cost sensitiveness is shown by comparing mode choice models with panel effects (mixed logit mode choice models with error components) and without panel effects (multinomial logit models). Inertia represents variability in mode choice between waves, measured as the effect of mode choice decisions made in a previous wave on the decisions made in the current wave. Additionally, all mode choice models include socio-economic and spatial variables but also mode preferences and life events. The effect of inertia on travel cost elasticities is measured by estimating mixed logit mode choice models with and without inertia effects. The main conclusion is that the inclusion of intrapersonal effects tends to increase cost sensitiveness whereas the inclusion of inertia effects decreases travel cost sensitiveness for car and public transport modes. Car users are identified as inert travellers, whereas public transport users show a lower tendency to maintain their usual mode choice. This paper reveals the inertia effects over four waves of repeated respondent’s data repeated yearly.

Incorporating MNL Model into Random Forest for Travel Mode Detection

Mode choice models have been used widely to forecast the relative probabilities of using available travel modes. These depend on mode-related and traveler-related characteristics. On the other hand, smartphones are increasingly being used to collect sensors’ data relating to trips made after selection of a suitable mode. Such sensors’ data may be correlated with decision-making process of travelers regarding travel mode selection. Discrete Choice Modelling is used to simulate this decision-making process by computing utilities of various travel alternatives, and then calculating their respective probabilities of being selected. In this paper, multinomial logit (MNL) mode choice model is utilized to enhance the prediction capacity of supervised learning algorithm i.e. Weighted Random Forest. To make the procedure less energy-intensive, GPS data was used only to locate the origin and destination of any trip, to be incorporated in mode choice model. Afterwards only accelerometer data was utilized in feature selection for the learning algorithm. One tenth of the classified data was used to train the algorithm whereas rest was used to test it. Results suggested that with incorporation of MNL, the overall prediction accuracy of learning algorithm was increased from 93.75% to 99.08%.

Exploring Preferences for Transportation Modes in the City of Munich after the Recent Incorporation of Ride-Hailing Companies

The growth of ride-hailing (RH) companies over the past few years has affected urban mobility in numerous ways. Despite widespread claims about the benefits of such services, limited research has been conducted on the topic. This paper assesses the willingness of Munich transportation users to pay for RH services. Realizing the difficulty of obtaining data directly from RH companies, a stated preference survey was designed. The dataset includes responses from 500 commuters. Sociodemographic attributes, current travel behavior and transportation mode preference in an 8 km trip scenario using RH service and its similar modes (auto and transit), were collected. A multinomial logit model was used to estimate the time and cost coefficients for using RH services across income groups, which was then used to estimate the value of time (VOT) for RH. The model results indicate RH services’ popularity among those aged 18–39, larger households and households with fewer autos. Higher income groups are also willing to pay more for using RH services. To examine the impact of RH services on modal split in the city of Munich, we incorporated RH as a new mode into an existing nested logit mode choice model using an incremental logit. Travel time, travel cost and VOT were used as measures for the choice commuters make when choosing between RH and its closest mode, metro. A total of 20 scenarios were evaluated at four different congestion levels and four price levels to reflect the demand in response to acceptable costs and time tradeoffs.

Evaluation of city-scale built environment policies in New York City with an emerging-mobility-accessible synthetic population

With the rise of smart cities, a number of new mobility services have emerged to drive changes in built environment policies. Up-to-date demand models are needed to capture the impact of these policies on emerging mobility-enabled travel patterns. The study explores modeling requirements to assess the impact of such built environment policies. A synthetic population of New York City with a tour-based nested logit mode choice model was developed with accessibility to bikesharing and ride hail services via smartphone ownership. The model results suggest Manhattanites have a value of time of $29/h, consistent with the literature. Smartphone ownership is positively influenced by income and negatively influenced by age, and in turn negatively impacts Citi Bike ridership relative to other modes available. The synthetic population is also applied to analyze two city-scale built environment scenarios: a hypothetical Amazon headquarter deployment and a Citi Bike service expansion. If Amazon succeeded in Long Island City, it would have increased the number of trips to/from that neighborhood by 239%, of which FHVs would grow by over 441% and transit by 294%. It would have led to an increase of peak morning trips from 5000 up to at least 8000. Citi Bike’s expansion plan would grow ridership by 92%, and if they were able to expand efficiently throughout NYC this would grow further to 210% over the baseline.

The analysis of influences of attitudes on mode choice under highly unbalanced mode share patterns.

The aim of the study is to examine the potential effect of attitudes towards physical activity on bus utility in the context of a rural area where studies have shown that the level and opportunity for physical activity are generally low. The need to analyze attitudes remains a strong motivation for the application of integrated choice and latent variable models. As such, we integrated attitudes towards physical activity with two specific attitudes towards bus and car use in a binary logit mode choice model between car and bus, looking at the area of Asuke, Japan. As we found data separation due to highly unbalanced mode shares in the input data, the choice models were estimated with the likelihood function penalized. While only a few parameters were found to be significant, arguably as a result of the unbalanced mode share pattern, attitude variables were almost unaffected by the data separation phenomenon. Whereas maximum likelihood estimates do not exist in the presence of data separation, the employed penalized maximum likelihood estimator was demonstrated to be a solution to this problem. Thus, we suggest that checking for data separation in the case of highly unbalanced mode share patterns is important and if data separation exits, penalizing the likelihood functions can be a solution rather than excluding out some irrelevant variables to avoid data separation. Overall, we found that attitude towards physical activity had a significant effect on bus utility, suggesting that policymakers could use this factor to connect transport and health policies.

Bus headway optimization with consumer surplus as a measure of societal benefit

ABSTRACT This paper derives the square root bus dispatch optimization developed by Newell from consumer surplus rather than the original cost minimization function. We then extend the model to consider dispatch-variant demand and outline its property relative to the original model by Newell. Demand elasticity is introduced via a multinomial logit mode choice model. Results are tested for City of Edmonton mode choice and ridership. The sensitivity of the function to wait time cost and passenger demand is tested. For the observed passenger demand and value of travel time, our analysis suggests there is a portion of riders who are captive and will suffer increasing wait time costs, but many passengers will make the choice to utilize other modes of travel when faced with increasing bus headways.

How sustainable is the growth of mass transit system in developing countries – an Indian perspective

In the modern urban world, transport researchers and policy makers are vehemently emphasizing the use of mass transit modes, primarily due to their distinct advantage in achieving congestion reduction, and for being environment friendly. In order to achieve high mass transit ridership, it is necessary to attract people to such modes so as to make them appealing and attractive when compared to personal motorized modes of transport. At the same time, it is also vital to assess different mass transit improvement alternatives and adopt a policy that leads to the sustainable development. India is currently witnessing a proliferation of new urban mass rapid transit systems, even in non-metropolitan areas. These are expensive to build and often do not realize their full potential even after decades of operations. This begs the question – can operational service quality improvement alternatives of existing public transit, provide users with benefits, comparable to a new mass rapid transit system? This paper evaluates the probability of modal shift to bus mode from non-mass transit modes on introduction of different service improvement measures applicable to Indian context. A multinomial logit mode choice model was developed based on a revealed preference dataset consisting of users’ travel behavior in existing traffic scenario where three mass transit modes – suburban rail, subway/ metro rail & bus, as well as the intermediate public transit mode of auto-rickshaw, where available. The model is used to predict impacts of three major policies - (1) existing network improvement, (2) lane management and (3) introduction of bus rapid transit system in terms of level of service improvements of bus transit and bus transit mode share. Results show that improvement in existing bus transit is more beneficial compared to lane management strategy and even comparable to bus rapid transit system considering the cost and time budget which may be used as a policy making tool by authorities for future city planning.

Modelling the impact of fused grid network design on mode choice behaviour

IntroductionThe fused grid (FG) is a an alternative model developed by the Canada Mortgage and Housing Corporation to combine the easy orientation and connectivity of the traditional grid pattern with the land use efficiency and quietness of the contemporary cul-de-sac pattern. One of the main distinguishing principles of the FG design relative to contemporary neighbourhood patterns are the FG's higher non-motorized network connectivity versus vehicle network connectivity. This paper reports on research related to the influence of FG design principles on non-motorized use for home-to-work commuting and home-to-shopping trips. MethodsThis study uses a macroscopic-level approach to investigate the impact of FG on mode choice behaviour through hypothetically retrofitting an existing neighbourhood in Kelowna, BC, using the FG design principles. First, the change in travel distance and time due to retrofitting the neighbourhood was estimated using ArcGIS. Then, the modal shift in home-to-work and home-to-shopping trips due to the change in travel distance and time was estimated using multinomial logit mode choice models that were developed using the 2013 Okanagan household travel survey. ResultsThe results suggest that the influence of travel time on choosing the auto mode is much stronger and more significant for home-to-work trips compared to home-to-shopping trips. The results also show that retrofitting the road network resulted in reducing auto modal share by 13 percent and increasing non-motorized modes by 64 percent for home-to-work trips. However, an insignificant shift in modal share towards non-motorized modes for home-to-shopping trips was found. ConclusionsIn conclusion, this study demonstrated the effectiveness of the FG principles in reducing auto use and increasing non-motorized modes use for home-to-work trips. In addition, the study revealed that infrastructure investments related to providing more accessibility for non-motorized users might have more impact on decreasing auto use compared to restricting vehicular network connectivity.

Likelihood of Motorcyclists Transferring to Safer Mode of Transport in Cairo, Egypt

Road traffic injuries and mortality are mainly caused by motorcycle crashes. Practically, 50% of people who meet their death in road traffic accidents (RTAs) are motorcyclists. The issue is increasingly articulated in progressing nations where the use of motorcycles has gained popularity in the past decades. Moreover, death and fatalities caused by accidents involving motorcyclists are also in the rise due to the increasing trend. Hence, motorcyclists are encouraged to use alternative modes of transportation that are safer in the attempt to minimise losses. As a result, a policy ought to be created to enhance urban transportation service and control motorcycle proprietorship. The current research that lays the groundwork aims to contribute a more elaborated analysis on motorcycle user mode decision conduct as well as an excellent comprehension of the conceivable efforts that can be taken to support motorcyclists to shift to a safer mode of transportation, particularly bus. In the current research work, the binary logit mode choice model was created for two elective modes in order to distinguish the separate practices of motorcyclists and bus users and assess their reactions to a situation that can minimize both time and expenses involved in bus travel. In addition, it should be noted that this paper surveyed a total of 327 travellers from Greater Cairo Region (GCR) in Egypt, the bus users were identified through revealed preference, while the motorcyclists were identified through revealed and stated preference surveys. In this case, travel time, travel cost, age, sexual orientation, income level, trip purpose, education level, and privacy significantly influence motorcycle user mode decision conduct. The likelihood of motorcyclists to utilize the use of buses was additionally analyzed dependent on a situation of minimized bus travel time and travel cost. These elements are very important in a program that attempts to draw in motorcyclists to utilize public transport, particularly bus. The outcomes can help the process of decision making on all levels in assigning the necessary assets prudently for the advancement of urban transportation services, reduced number of road traffic crashes, and increased road safety. This examination, which is the first of its sort in Egypt, assesses the model choice behaviour for motorcyclists.

Modeling the Effect of New Commuter Bus Service on Demand and Impact on Greenhouse Gas Emissions: Application to Greater Boston

The transportation sector is considered one of the major contributors to greenhouse gas (GHG) emissions in metropolitan areas. Any efforts to reduce these emissions require strategic management of multiple transportation modes. This paper presents a method to identify opportunities to reduce GHG emissions by expanding commuter bus services and providing incentives to shift commuters from private cars to transit. The approach uses a nested multinomial logit model for mode choice in a region where modes include driving alone, carpooling, walking, cycling, and using four possible transit modes (ferry, commuter rail, rapid transit, and bus) by walk-access or drive-access. A model of existing conditions was calibrated with data from the Boston metropolitan area. Using an emission factor model based on average speeds from the California Air Resources Board, the net effect of a new commuter bus service on GHG emissions from transportation is estimated. Potential GHG reductions are weighed against the capital and operating costs of new transit services to quantify the cost-effectiveness of new commuter bus services for isolated origin-destination pairs. This modeling framework is used to optimize fares and bus frequency to identify the corridors with the most cost-effective potential for GHG reduction. Results are presented for the Boston region, demonstrating the feasibility of implementation and the potential magnitude of benefits for cost-effectively reducing GHG emissions associated with transportation. The method is general and can be applied in other cities around the world.

Mobility impacts of automated driving and shared mobility

This paper presents a model specifically developed to explore the mobility impacts of connected and automated driving and shared mobility. It is an explorative iterative model that uses an elasticity model for destination choice, a multinomial logit model for mode choice and a network fundamental diagram to assess traffic impacts. To the best of the authors’ knowledge, it is the first model that combines a network fundamental diagram with choice models. A second contribution is the inclusion of automated vehicles, automated (shared) taxis, automated shared vans and new parking concepts in the model as well as the way in which they affect mobility choices and traffic conditions. The insights into the direct mobility impacts are the third contribution. The short computation time of the model enables exploration of large numbers of scenarios, sensitivity analyses and assessments of the impacts of interventions. The model was applied in a case study of the Dutch Province of North-Holland, in which the potential impacts of automated and shared vehicles and mitigating interventions were explored. In this case study, four extreme scenarios were explored, in which 100% of the vehicles have SAE-level 3/4 or 5 and people have a low or high willingness to share. The extremes were chosen to get insights into maximum effects. The results show that if automated vehicles and sharing are accepted, it is likely that there will be considerable changes in mobility patterns and traffic performance, with both positive and problematic effects.

Stated choice model of transport modes including solar bike

In the Netherlands, e-bike ownership and use has rapidly increased over the last decade. A new type of e-bike, the solar bike, has recently been developed. The solar bike is an electric bike with solar panels in the front wheel that charges through sunlight. The aim of this study is to gain more insight in the factors affecting people’s choice between different transport modes, including car, public transport, regular bike, e-bike and solar bike. Based on a stated choice experiment among 308 respondents, a mixed logit error components model for transport mode choice was estimated. The results show that the solar bike is preferred for medium-length trips during daylight and in good weather. Land-use attributes such as good bike lanes, secured bike parking, congested roads and paid parking also have a positive effect on choosing a solar bike over a car. In addition, a latent class model was estimated to segment respondents according to their base preferences for transport modes. Three segments were identified: a segment with a preference for the solar bike, a segment of car lovers and a segment with a preference for public transport and a regular bike. Chi-square and ANOVA tests show that solar bike affinity is related to being female, older, Dutch, and having a positive attitude toward e-bike, solar bike, innovation and the environment.

Modeling the effects of real time traffic information on travel behavior: A case study of Istanbul Technical University

This article adds to the literature on the investigation of choice behavior of travelers under the real-time traffic information acquired through some traffic applications such as GPS navigation devices for car, mobile traffic applications and radio traffic reports on traveler behavior on highways. Self-deviced survey of travelers was conducted for the civil engineering undergraduate – graduate students, academicians and supporting staff at Istanbul Technical University, Turkey in 2016. Multinomial logit mode choice model of the decision making for travel and commuter responses to traffic information were estimated separately in two different commute modes, including private cars and public transit. The attributes that influence travelers’ decision-making patterns were broadly categorized into three groups, which were socioeconomics, travel and technological characteristics. The analysis of the results indicated that travelers who obtained traffic information from some traffic applications were more likely to switch their route with respect to their different characteristics. Moreover, the travel pattern of the commuters regarding whether to change their choice of route or not varied with respect to their aforementioned characteristics as well as their selection of commute modes. The results of this research could also help to develop vehicular communication systems such as vehicle-to-infrastructure V2I communications.