How does travel satisfaction sum up? An exploratory analysis in decomposing the door-to-door experience for multimodal trips

This rule is based on the idea that a simple average of all trip legs can provide a good estimate for the whole trip satisfaction.

Duration-weighted moving (DWM), considers in-vehicle/walking times only. Therefore these rules neglect the importance of waiting times in constructing an overall evaluation metric for the trip. Miron-Shatz (2009) and Suzuki et al. (2014) in sociology and transport fields respectively found that the summation of instant utilities made by means of the duration weighted rule provided the best fit.where \(t_{{l_{j,i} }}^{m}\) is in-vehicle/walking time for leg l_j,i.

Duration-weighted complex (DWC) embeds to Eq. 2 traveler’s waiting times.where \(t_{{l_{j,i} }}^{w}\) is waiting travel time for leg \(l_{j,i}\) and \(\omega\) is the multiplier applied to each waiting time as a penalty.

Duration-weighted Complex can vary with respect to the weight or penalty assigned to the waiting times. Queue waiting negatively impacts overall perception of the trip (Smidts and Pruyn 1994), however the magnitude of the influence is still controversial. Hine et al. (2001) found that traveler’s perception of waiting times varied amongst travel modes being higher for train than for buses. Some other authors range traveler’s perception of waiting times between 1.52 and 4.4 times longer than in-vehicle time (Mishalani et al. 2006; Dziekan and Kottenhoff 2007). Therefore waiting time weights, ω, of 1, 2, 3 and 4 are specified in this work.

This rule tests whether the last trip experience, the one associated with the last trip leg, alone explains overall travelers’ evaluation. Previous studies report contradictory results concerning whether end-effect exists and have a significant impact on the overall experience. For example, Finn (2010) in an experiment involving a learning experience, Diener et al. (2001) in an experiment that explored how the ending of a life influenced the desirability of that life; and Carmon and Kahneman (1996) in an experiment related to queuing experiences provided evidence supporting their existence. However, Miron-Shatz (2009) for common activities undertaken during a day, and Tully and Meyvis (2016) for sound stimuli and an obstacle race, did not find evidence for the existence of end effects.Hence, overall satisfaction is determined by the last trip leg.

This rule asserts that the sequential order in which an experience is presented affects how it is evaluated. The first and last episodes of an experience, primacy and recency effects, are the better remembered parts of an experience and the ones that solely impact the overall evaluation of the experience. These effects were proven by Page and Page (2010) studying the evaluation of a TV show; by Miller and Krosnick (1998) for the impact of candidate name order on elections outcomes; and by Bruin de Bruin (2005) when evaluating end-of-sequence and step-by-step evaluation procedures used in Eurovision song contest.

This rule examines whether the most salient experience is the most representative of the whole trip. An exceptional episode can be caused by a wide variety of events such as: extreme on-board crowding, severe weather conditions which make the waiting time at a stop unpleasant, being able to find a seat in rush hour, feeling unsafe or experiencing longer or shorter travel times than expected. The rule is defined as the satisfaction with the trip leg with the largest deviation from the average overall satisfaction:This salient event is the one employed by Suzuki et al. (2014).

The deviation can be either positive or negative depending on whether the episode is pleasant or unpleasant (Fredrickson and Kahneman 1993). The latter contradicts the findings made by Friman et al. (2001) who concluded that negative episodes, or as they call them negative critical incidents, have a higher impact on overall and attribute specific cumulative satisfaction, than positive ones.

Given that satisfaction evaluations are partly based on expectations (Parasuraman et al. 1991; Grönroos 1994) and that travel time is one of the main components for the evaluation of a trip (Ettema et al. 2016), deviation from expected travel time is the salient event presented in the next rule:where \(\widehat{{t_{{l_{j,i} }}^{m} }}\) and \(\widehat{{t_{{l_{j,i} }}^{w} }}\) are the expected in-vehicle or walking time (moving time) and waiting time, respectively, on a given trip leg. Given that both, moving and waiting times might be perceived very negatively when delays are present (Carrel et al. 2015). The deviation can be either positive or negative. If no deviation from the expected time is observed then the equal-weighted averaging rule is applied.

This rule asserts that the recollection of a past overall experience is best aggregated by averaging only two distinct moments; the most salient event (peak) and the latest experience (end). Therefore, the effects of the duration of an experience upon retrospective evaluation are neglected (Fredrickson and Kahneman 1993; Ariely and Loewenstein 2000).

A large amount of empirical evidence in different fields supports the peak-end rule. For example, Fredrickson and Kahneman (1993) demonstrated it for film watching, Redelmeier and Kahneman (1996) for painful medical practices, and Baumgartner et al. (1997) for rating film and television commercials. However, Miron-Shatz (2009) did not reach the same conclusions for day-long experiences. A peak_max-end rule is formed by the combination Eqs. 4 and 6:And the peak_exp-end is formed by the combination of Eqs. 4 and 7:

The success of either of these rules may have the following implications for stakeholders. A better goodness-of-fit of the equal-weighted averaging rule may signify that improvements should be equally distributed over all parts of the travel experience. The success of the duration-weighted moving rule may imply that travel experience aspects of the modes, when in movement, should be prioritized. Travel experience aspects may include instrumental and non-instrumental variables. The implications of a successful duration-weighted complex rule might be interpreted as the largest importance given to trip legs comprising waiting and/or transferring times, and thus of those involving transit modes. The higher the waiting time penalty the higher the importance attached to transit legs. In this case, the corresponding stakeholders should focus on improving interchange facilities and reducing and uplifting the waiting time perception. The success of the end rule may suggest that stakeholders need to care only about ensuring that travelers have a pleasant and satisfactory experience in the final leg of their door-to-door trip. In a typical 3-leg trip this implies focusing on last-mile facilities. In line with the former, the success of the serial position rule may suggest that first- and last-mile facilities should be prioritized. Travel time reliability and adhering to expected travel times should be targeted if peak-exp travel time (rule 9) is found to better explain overall travel satisfaction. The negative effect that disruptions and negative salient events have on overall satisfaction may indicate the need to focus on them if peak-max satisfaction rule obtains the highest goodness-of-fit.

The abovementioned satisfaction aggregation rules are illustrated using an example. Figure 1 displays an example of a door-to-door trip including all the relevant data needed to aggregate trip leg satisfactions into overall travel satisfaction evaluations. The door-to-door trip received a reported overall satisfaction of 4, in a five-point likert scale (Fig. 1a). The trip comprises three trip legs and three stages (access, main and egress), each of them shown with its respective reported satisfaction value in the labels (2, 5 and 3 for access, main and egress respectively). The passenger walks from and to bus stops. The perceived travel time of the entire trip is 36 min including 4 waiting time minutes, while traveler expected travel time (T.T) for this trip was 31 min. The percentage time difference shows the percentage difference of the perceived travel time compared to the expected travel time. The latter is calculated at a trip leg level and as specified in Eq. 7. The percentage satisfaction dev. is the ratio of the trip leg satisfaction value to the simple average of all trip legs’ satisfaction values, as specified in Eq. 6.

Using the example data shown in Fig. 1a, 1b displays the values obtained when applying the twelve satisfaction aggregation rules defined in the previous sub-sections. For instance, the calculation of duration-weighted complex with a penalty of 3 moving minutes per waiting and/or transferring minute is calculated as follows: ((5*2) + (((4*3) + (20))*5) + (7*3))/36 = 4.34.

Table 1 presents the summary statistics of the socio-demographic characteristics and mobility patterns of the survey sample by purpose, main travel mode and trip complexity. The percentage of respondents per category is shown in bold and the share of missing values and non-responses is displayed in brackets. Data of respondents with missing values were used only in the exploratory stage and were excluded in the model estimation phase.

Approximately two thirds of the respondents are young adults (65%)—age 18–34, who report their commuting trip (67%). In addition, young adults use more frequently than others private vehicle or non-motorised modes (other) as their main travel mode. About 7% of travelers reported to have some type of disability, and their share doubles when considering only non-commuting or bus trips (14% each). Compared to the average duration of all trips (58 min), trips travelled by train (114 min) and non-commuting trips (76 min) have longer average duration and larger variation in trip duration (see SD in the duration row in Table 1). This might be explained by the conjunction of a wider diversity of non-commuting trip purposes, and by the prominence of train (suburban, regional and inter-city) as the main mode. The unexpected high share of unemployed and low income commuters results from the large share of students within this group (40%). Bus is the main mode for 42% of the trips that consist of 2 legs, while metro is the main mode for 84% of the most complex trips comprising of at least 3 legs.

A modification of the interconnectivity ratio proposed by Krygsman et al. (2004), which we define as the proportion of access and egress times to the main leg travel time, is employed in this paper to further characterize the trips by showing the ratio of time spent by travelers with regard to the main leg. The interconnectivity ratio provides an insight on the distribution of travel time between (primarily) non-motorised modes and the main travel mode. It is expected that most of the travel time is spent in the main mode, and thus yielding ratios below 1. With the exceptions of trips by metro (1.15), 4-leg trips (1.30) and trips with transfers (1.02), generally travelers spend less time in access and egress legs together than in the main mode. A visual examination of the modes used in the access and egress legs displays the importance of non-motorised modes (60–63%) which are followed by a combination of non-motorised modes and transit modes (mixed transit, 18–21%), and exclusively transit modes (11–16%). Non-motorised modes only are particularly preponderant at bus and 2-leg trips, while their presence is much smaller in trips with 4 or more legs, with a transfer and with train as the main mode.

Table 2 shows the means and standard deviations for overall satisfaction with the entire trip and for the values obtained for the 9 different trip categories when applying each of the satisfaction aggregation rules, as defined in Eqs. 1–9. Waiting time weights, ω, of 1, 2, 3 and 4 are specified in rule 3, DWC (Duration Weighted Complex) which weights leg satisfaction by its duration. The travel mode category ‘other’ has been dropped out of the analysis since it encompassed very different travel modes (walk, bike, tram and private vehicles) and the results would be difficult to interpret. In addition, it is not possible to calculate the peak-and-end rules for 2-leg trips, thus they are shown in the table with NA (not applicable).

As observed in Table 2, compared to the entire dataset, overall travel satisfaction is slightly higher for commuting trips and significantly higher for trips with 3 legs and trips with metro as the main mode. Highest overall satisfaction for commuting and metro trips is in line with some previous findings (Woldeamanuel and Cygansky 2011 and Cao et al. 2015, respectively). Coincidentally, travel duration of all these trip categories is the shortest compared to their counterparts. This might be in line with previous research (e.g. Ory and Mokhtarian 2005) which reported that the level of travel satisfaction is a function of travel distance; and that the longer one travels the less satisfied one becomes.

The mean values of overall satisfaction yielded by the various aggregation rules manifest a large range, as large as 0.8 points for trips of 4 or more legs and as narrow as 0.2 points for 3-leg trips. In the following section we investigate which of the proposed rules provides the best estimate for overall satisfaction for different trip categories.

A series of regression models were estimated with “Overall satisfaction with the whole trip” as the dependent variable, and with each of the satisfaction aggregation rules defined in Sect. 3 determining the specification of the independent variables. Since overall satisfaction is an ordinal variable, ranging from 1 (very unsatisfied) to 5 (very satisfied), ordered logit models are most adequate. In terms of random utility theory, ordered logit model can be expressed as:where \({\text{y}}_{\text{k}}^{ *}\) is the latent dependent variable of individual k. \({\text{X}}_{\text{k}}\) is the explanatory variable set of individual k, which includes a satisfaction aggregation rule for individual k. Note that the intercept is dropped for identification issues. \(\upbeta\) is the corresponding vector of parameters to be estimated. \(\upvarepsilon_{\text{k}}\) is the error term which is assumed as an identically distributed logistic error-term. The latent dependent variable is then associated with the observed dependent variable, y_k (5 likert scale overall satisfaction), with m = 1,…, 5, defined as follows:Aggregation rules for all different trip configurations are modeled. The total number of estimated models amounts to 130 (12 aggregation rules * 11 trip configurations − the 2 peak-end-rules for 2 leg trips). Table 3 shows McFadden R2 square (Rs) in the first row and significance values (Sig.) in the second for each model. As can be observed, the very few insignificant results at a 90% confidence interval are found for the end rule. The magnitude of the R2 square coefficients ranges between .023 and .362 points being highest for metro trips and lowest for bus and 3-legged trips. These coefficients are in general lower than it could have been expected when specifying a model with an overall satisfaction measure calculated by means of a very diverse set of aggregation rules. This may suggest that either the rules tested do not aggregate well door-to-door travel satisfaction or that overall travel evaluations are influenced by factors other than the aggregation of their components.

In general, the average weighted rules that consider both moving (in-vehicle/walking) and waiting times (DWC) are the best predictors of overall remembered utility, highlighting the importance of waiting times. However, notable exceptions are found for different trip configurations. The peak rule in which the salient event takes place in the leg with the maximum deviation from the expected travel time (Peak_exp) is found to be the best aggregation rule for non-commuting trips (.155). Additionally, trips made with bus as the main mode are better aggregated (.132) by the peak rule which salient event takes place in the leg with the maximum deviation from the average overall satisfaction (Peak_max). Furthermore, overall satisfaction with 2-leg trips is best reproduced using either the equal-weighted averaging or the serial position rules (.281) since in this case they are essentially the same.

Model estimation results show that for a large number of multi-modal trip configurations, normative rules are superior in retrospectively aggregating experiences to heuristic rules. In particular, this is true for duration-weighted averaging rules with a penalty of 3 and 4 times relative to a moving minute. However, for certain trip configurations (2-leg, bus, non-commuting) some heuristic rules, including the peaks and the peak_max-end, yield the best goodness-of-fit.

Evidently, the serial position and the end rule are the worst-performing aggregation rules. However, a remarkable exception is found with the serial position rule for the least complex multi-modal trips (2 legs), where this rule and the equal-weighted averaging rule prove to be best. This might indicate that the shortest multi-episodic experiences are easiest to recollect due to their simplicity, and respondents are thus less prone to recall biases. Additionally, some other factors such as their presumably lesser exposure to waiting times and chances to experience negative and salient events may reduce the likelihood of other aggregation rules to prevail.

The performance of three other measures (DWM and peak-end-rules) is very modest. The poor results yielded by the average-weighted rule that considers in-vehicle/walking times only (DWM) were expected. These results reinforce the notion that moving times alone, and thus the quality of the services and factors influencing the moving experience alone, do not explain well traveler’s perception of the overall trip (e.g. Ariely 1998).

This section studies the strength of the relationship between overall satisfaction and the different trip legs and aims to unveil the most relevant leg for bus and non-commuting trips.

A correlation matrix was constructed in order to identify the strength of the relationships between overall trip satisfaction and the satisfaction with different trip stages, as well as to study the extent to which the trip stages are inter-correlated. Multimodal transit trips consist of two or three stages: a main leg and an access and/or egress stage. Access and egress stages can be comprised of a single or various legs before or after the main leg respectively. Trip legs are aggregated into stages by means of simple average of the trip legs.

Figure 2 and Table 4 present cross-correlation matrices for different trip configurations with regard to their purpose, travel mode, trip complexity and presence of transfers, with each satisfaction item represented as a node and the correlation between two items illustrated by a link. The cross-correlation graphs were visualized using the NodeXL excel add-in and offer an intuitive glimpse of the relationships between the various factors. In Fig. 2, correlations with the overall trip satisfaction are highlighted using red lines while correlations among trip stages are displayed in blue. The line thickness corresponds to the degree of correlation as follows; correlations above 0.7 are shown with a solid line, those between 0.5 and 0.7 with a dashed line and those between 0.3 and 0.5 with a dash and dot line. The coefficients shown are at a 90% confidence interval and are always positive.

A number of general trends can be observed in Fig. 2. When compared to access and egress legs, the main trip leg is more strongly and consistently correlated (> 0.5) with overall trip satisfaction. In some of the trip configurations where DWC measures are best aggregating the travel experiences (Fig. 2: a-General, e-Commuter, b-Metro, c-Train, i-4 legs and k-Transfers), this higher correlation could be due to the extensive share (84.6%) of transit modes used in the main trip leg. Transit modes which generally consist of waiting times which in turn are perceived with a higher weight than moving times. In the remaining trip configurations with successful DWC rules, however, the lack of significance and the weakest correlations existing between access/egress legs and overall satisfaction (Fig. 2: j-Without transfers and h-3 leg trips) might be attributed to the very large share of non-motorised modes used in the access (73 and 81%) and egress legs (64 and 82%) respectively. Being non-motorised modes are characterized by their lack of waiting time and their travel time reliability.

While peak rules were found to be most adequate for explaining satisfaction with bus and non-commuting trips, the impactful leg associated with the peak experience remained unknown. Cross-correlations, however, allow to determine the most probable impactful leg. For bus trips, the main leg is identified as the leg with the maximum deviation from the average satisfaction since this is the only leg showing a moderate but significant correlation coefficient (d-Bus) with overall trip satisfaction. For non-commuting trips, the main leg´s correlation is 0.59, far over the 0.37 of access or egress legs and thus the former should be considered as the most deviant and relevant one. Commuting and non-commuting trips show very similar correlation coefficients: very strong correlation between access and egress stages and between main and overall satisfaction. The main difference pertains to the weaker correlations found for non-commuting trips between access and egress stages and main and overall satisfaction. This may indicate that commuters perceive their door-to-door trip as a more integrated experience.”

Similar weights attributed to the main and access/egress legs in constructing an overall evaluation of the trips with transfers is made evident in Fig. 2c-Train, where all legs exhibit a very high correlation (> 0.5) with overall satisfaction. Furthermore, in the least complex trips (2-leg) all legs are similarly important and correspondingly all travel modes and trip stages are equally important, as indicated by the performance of the aggregation rules.

Interestingly, an examination of all correlation matrices shows that access legs are more strongly correlated with overall satisfaction than egress legs. This may suggest that the first trip stage influences the overall evaluation more than the last stage (egress) does and that therefore access should be prioritized over egress. The only study that commented on possible differences between the importance of access and egress trip stages (Van der Waard 1988) found that access walking time has greater influence on bus trip route choice than egress walking time.

Finally, a very strong inter-correlation is found across the board between access and egress legs (> 0.7) which may support the belief that first and last miles could be roughly considered as a single entity. However, train trips are a noticeable exception to this assertion. The very different access/egress’s travel mode composition generally occurring in train trips (i.e.: Givoni and Rietveld 2006) is also a reality in our data where non-motorised modes and other modes, mainly private vehicle, represent a 60% of the modal split in the access legs while mixed transit and transit are used by 65% of travelers for egress (see Table 1). A plausible explanation to their negligible correlation coefficient is the specific characteristics inherent to each mode which are differently evaluated by travelers.

By creating generalized aggregation models the present section aspires to go a step further into the study of the strength of the relationship between overall travel satisfaction and the trips stages. This is done by considering the normative rules only in aggregating individual trip legs’ satisfaction into trip stages’ satisfaction since they include all trip legs. These generalized aggregation models would assist stakeholders in better evaluating the percent relative weight on overall travel satisfaction assigned to each trip stage.

A series of ordered logit regression models were estimated with “Overall satisfaction with the whole trip” as the dependent variable, and with trip stages’ satisfaction values as the independent variables. Each of the normative rules are employed to aggregate trip legs’ satisfaction into satisfaction with access and egress trip stages. The main trip stage corresponds to the longest trip leg. There are three conceptual models: one for trips including all trip stages (M1—all trips stages), and two for trips including main and either access (M2) or egress (M3) stages. By combining aggregation rules and trip leg inclusion options, the total number of estimated models amounts to 18. Table 5 shows the estimated coefficients of the independent variables (Coeff), the relative weight percent of the trip stages on overall satisfaction (W) and the McFadden R2 square (Rs). With few exceptions, the results are significant at a 90% confidence interval.

The strength of the Rs coefficients is not directly comparable across conceptual models since they involve a different type of trip, combination of independent variables and number of respondents. However, Rs are used to identify the best aggregation rule for each of the conceptual models. Equal-weighted average (M1 and M3) and duration-weighted moving (M2) are the models explaining the better overall travel satisfaction and thus hereinafter are taken as the reference models. This finding might seem to be in contradiction with Sect. 5.2 results where the IVT and waiting and/or transferring times of the trip legs matter. However, in this case the aggregation is made at a trip stage and not a trip level. In addition, access and egress are made up of few components (between 1.2 and 1.3 trip legs) and their trip leg’s duration (moving and waiting times) is shorter than the main leg. Therefore, all together, the impact of the trip leg duration is less consequential. The similar importance given to each of the trip legs of access and egress seems to indicate that travelers naturally aggregate and clump together the components of these trip stages in a simple way.

In general, the relative weight of the main leg is higher (M1—100% and M2—54%) than that of the remaining trip stages. This is in consonance with the results obtained in Sect. 5.3. However, egress explains about 2/3 of overall travel satisfaction in M3. The fact that access and egress trip stages are made up of a composition of trip legs, indicates that at a trip leg level their relative weight is smaller than that obtained at a trip stage level.