Users views on current and future real-time bus information systems

Transcription

1 JOURNAL OF ADVANCED TRANSPORTATION J. Adv. Transp. 2013; 47: Published online 24 August 2012 in Wiley Online Library (wileyonlinelibrary.com) Users views on current and future real-time bus information systems Md. Matiur Rahman, S.C. Wirasinghe and Lina Kattan* Department of Civil Engineering, Schulich School of Engineering, University of Calgary, Calgary, Alberta, Canada SUMMARY Actual bus arrival times often deviate from the posted schedules due to a variety of factors; hence, providing real-time bus information can improve service quality. This study examined users views and perceptions towards the possible future availability of real-time bus information systems in Calgary, Alberta, Canada. A face-to-face paper-based survey was conducted to collect the data. Various statistics and methods, such as ANOVA tests, ordinal regression and binary logistic regression, were used to analyse the data. The results showed that 35.5% of the respondents either agreed or strongly agreed that the current information system deterred or discouraged them from using public transport. In addition, a significant portion of respondents (82%) stated that they board the first arriving bus, even though it may take a longer in-vehicle time to complete the trip, because of uncertainty regarding the arrival time of the next alternative bus with a shorter in-vehicle travel time. A majority of the respondents (88%) indicated that real-time transit information would not be necessary if bus headways are less than 10 minutes. As for preferred information content, information on the next bus arrival time received the highest priority. In general, Light Rail Transit (LRT) users showed the least interest in real-time information. Women, younger riders, current car users and infrequent transit users showed a higher interest in real-time information. Display boards at bus stops were perceived to be the most preferred medium to get en-route information, whereas a website/call centre was stated to be the preferred media for pre-trip information. Copyright 2012 John Wiley & Sons, Ltd. KEY WORDS: real-time bus information; bus users views; next bus arrival time; logistic regression 1. INTRODUCTION Although transit service reliability, such as schedule adherence, can be improved via operational control, transit operations are inevitably disrupted by various stochastic and uncontrollable factors. Actual bus arrival times often deviate from the posted schedules because of a variety of factors, such as traffic congestion, variation in dwell times at stops, weather conditions, incidents and driver behaviour. The lack of accurate information on bus arrival times can result in longer waiting times for passengers or increase the chance of missing the desired bus. The relative unpredictability of bus service makes it less attractive. Thus, providing timely updated real-time transit schedule information, such as expected bus arrival times, departure times and information on unexpected major delays, should improve waiting times especially at the bus stop and increase ridership [1,2]. The dissemination of timely updated accurate transit schedule information is useful for both transit users and agencies. Such information can assist transit agencies in restoring service disturbances. From the users perspective, real-time schedule information can reduce waiting time and anxiety while waiting for buses. Total travel time from origin to destination can also be reduced, because real-time information can help users arrive at the bus stop closer to the actual bus arrival time, regardless of whether the chosen bus is early or late. *Correspondence to: Lina Kattan, Department of Civil Engineering, Schulich School of Engineering, University of Calgary, Calgary, Alberta, Canada. lkattan@ucalgary.ca Copyright 2012 John Wiley & Sons, Ltd.

2 USERS VIEWS ON CURRENT AND FUTURE REAL-TIME BUS INFORMATION SYSTEMS 337 In this era of rapid technological change, there is considerable scope to provide relevant real-time transit information. The application of intelligent transportation system technologies in transit systems offers the opportunity to obtain both real-time and archived transit data. Automatic vehicle location and automatic passenger counting systems are two examples of such technologies [3 7]. The archived and real-time transit data can be utilized to provide updated information to riders. Recent research efforts have shown that predicted transit arrival time might be provided with relatively high accuracy [8 12]. Such transit information can be disseminated through different media, such as electronic display boards at bus stops, the Internet, a call centre, personal digital assistant (PDA)/smart phone applications, short message service (SMS) and Twitter. Providing real-time transit information is often associated with high installation and operational costs, as these systems rely heavily on the use of Global Positioning Systems, information technologies and real-time communication systems. Given the large investments required for such systems, it is necessary to understand their likely outcomes. Because the perceived and measured benefits of real-time transit information system can vary from place to place, it is useful to understand the users likely responses and preferences before deployment in a specific city. In this study, a face-to-face paper-based survey has been conducted and analysed to examine users views and perceptions towards possible future availability of real-time bus information systems in Calgary, Alberta, Canada. Very few studies have attempted to estimate the threshold value of transit headway below which real-time transit information would no longer be perceived as important by transit passengers or examined the error margins that people are willing to accept from real-time information systems [13,1]. The rest of this paper is organized as follows: Section 2 is a review of the relevant literature. Section 3 presents an overview of the study area, whereas the data collection and research methodology are discussed in Section 4. Results are summarized in Section 5. Some concluding remarks and recommendations for policy level implications are presented in Section LITERATURE REVIEW Users responses to real-time transit information have been widely studied. The literature on the effectiveness of such real-time transit information is mixed, with outcomes dependent on the socioeconomic characteristics of respondents, transit service characteristics and trip characteristics. The outcomes of a number of studies support the hypothesis that real-time transit information plays asignificant role in attracting transit riders [14 21]. On the other hand, other studies have suggested more conservative modal shifts as a result of real-time transit information provision [22 24]. Hickman and Wilson [25] investigated how real-time transit traveller information could impact the path choice of users of a corridor. They found that real-time transit information could not improve travel time or system time variability of a transit system, because most riders were not willing to change their travel patterns. Regardless of increased transit ridership, studies that examined passenger perceptions and attitudes towards the provision of real-time transit information showed that transit riders appreciate getting information on the arrival time of the next bus and the length of any delay [1,21,26 30]. Furthermore, various studies demonstrated a reduced perceived waiting time at a bus stop associated with the provision of real-time at-stop displays [1,27,30,13]. These studies showed that passengers overestimated their waiting times only by 9 13% when real-time information was given, as compared with 24 30% without the information. Schweiger [13] reported a perceived wait time drop of 26%. Results from various research efforts showed that the impact of real-time transit information was mostly correlated with travellers socioeconomic characteristics, trip and route characteristics, weather conditions and network familiarity (i.e. whether the travellers are familiar with the transit routes needed to complete their trips) [17 20]. Wardman et al. [31] observed that real-time information at transfer points was especially important for occasional users. Nijkamp et al. [30] segmented the sample based on frequent and infrequent bus users, age categories and employment status, because at-stop displays impacted these groups differently. Lehtonen and Kulmala [27] suggested that the overall effects were better for bus routes with lower frequency.

3 338 M. M. RAHMAN ET AL. Wirasinghe [32], Wirasinghe and Liu [33,34] and Liu and Wirasinghe [35] have shown that the optimal reliability for a bus route that minimizes the total cost of the system is dependent in part on the value passengers give to delayed travel time. Better estimates of real-time bus arrival time is likely to reduce passenger anxiety and, hence, the perceived cost of delayed travel time, thereby reducing the extra slack time that is added to the schedule in the planning stages of schedule construction. 3. STUDY AREA Calgary is Canada s fourth largest city with a population of 1.23 million [36]. The city s population growth has created new neighbourhoods of low-density single-family residences. According to Grabeland [37], the City of Calgary is the size of New York City, but with only one tenth of New York s population. The resulting urban sprawl combined with the segregation of land use types creates a big challenge for a good quality public transit service. The morning peak period work trip transit modal split to the downtown is as high as 42% [38]. However, the city has the following modal splits: auto = 77%, public transit = 8.6%, walking = 12.4% and bicycle = 1.9% [39]. This low-transit modal share may be due mainly to the predominance of low-density areas that are underserved by public transport. Nevertheless, even when the low-density suburbs are not considered, the overall transit modal share in the city is still low [40]. Habib et al. [40] found that Calgarians value reliability and convenience over ride comfort in choosing public transit. Thus, there is a substantial opportunity to attract more people to transit and increase the modal share by providing more reliable real-time bus information. Calgary has a public transit network consisting of CTrain (Light Rail Transit, LRT), regular bus, bus rapid transit (BRT) and community shuttle service within the city. Calgary Transit, which is owned and operated by the City of Calgary, is the public transit agency that is responsible for planning, operating and maintaining the transit network. Calgary Transit, previously called the Calgary Municipal Railway, began its operation in 1909 in a community of with 26 km of track and 12 electric streetcars. In 1940, the streetcars were replaced with motor buses and electric trolley buses. From 1950 to 1975, Calgary grew to a population of almost half a million people. The bus and trolley systems were expanded to meet that demand; and, during the late 1960s, trolley buses were replaced with diesel buses. CTrain service commenced in 1981 with the 10.9 km south line from Anderson Road to 7th Avenue SW. To keep pace with the rapid citywide expansion, Calgary Transit underwent a major expansion and improvement of its transit service between 1995 and Calgary Transit provides several kinds of regular transit services, as well as some special services, such as door-to-door transportation for reduced mobility residents and two BRT routes that provide higher frequency and limited stop service. Calgary Transit has 159 transit routes; and, today s transit fleet is made up of 222 regular buses, 534 low-floor buses, 37 articulated buses, 85 community shuttle buses and 157 light rail vehicles. Calgary Transit currently provides 94.2 million passenger journeys annually covering km 2 [41]. Calgary Transit offers customers various methods to access its scheduled transit route information. Information is available on the Calgary Transit website, through its Teleride system, which is an automated telephone information system that provides schedule information for each bus stop [42], postings at LRT stations and major bus stops, and in printed pocket schedules. The Teleride system was implemented by Calgary Transit in May 1987, and 15.1 million callers used it in 2005 [42]. Google Transit also offers an alternative way to get schedule times and trip planning for Calgary Transit. Currently, Calgary Transit is also Tweeting updates about detours and delays because of road construction along various transit routes. In 2008, a majority of customers reported having used the Calgary Transit website (51%), followed by the Teleride system (47%), information posted at CTrain stops (33%), information posted at bus stops (31%) and transit system maps (30%). Less commonly used were the pocket schedules (17%), the customer call centre (14%) and the 7th Avenue customer centre (10%) [43]. According to the 2009 Calgary Transit customer satisfaction survey, the Teleride system was used most frequently by respondents (an average of 5.3 times per month), followed by the Calgary Transit website (1.7 times per month) and information posted at bus stops (1.7 times per month) and information posted at CTrain stations (1.4 times per month) [43]. This survey also indicated that the use of almost all

4 USERS VIEWS ON CURRENT AND FUTURE REAL-TIME BUS INFORMATION SYSTEMS 339 of the available static information sources declined from 2002 to 2009 [43]. The City of Calgary is now considering the development of a real-time transit information system to enhance the quality of its transit system [44]. 4. DATA COLLECTION AND RESEARCH METHODOLOGY Various data collection techniques can be used to elicit commuters views regarding the dissemination of real-time transit information. The data can be based on a comparison of actual transit ridership before and after the implementation of real-time transit information system or on surveys. By comparing observed transit ridership before and after the implementation of such systems, Body [45] with Schweiger and Cross [46,13] found that the ridership increases for the routes that are provided with real-time information. Data collected through surveys can be either in the form of stated preference (SP) or revealed preference (RP) or a combination of both. An SP survey provides hypothetical advanced traveller information system scenarios and obtains the respondents views through questionnaires. An RP survey is based on travellers actual choices, which are usually obtained from diaries and field experiments. A number of decision choice models have been developed from one survey method or the other, in order to identify possible contributing factors that affect riders responses [47 53]. This study s survey was designed with the objective of collecting transit users (frequent and infrequent) perceptions about the quality of the current bus information and their preferences for a real-time system that would be deployed in Calgary. The survey included 18 questions grouped into six parts: (i) socioeconomic information; (ii) trip characteristics; (iii) familiarity with different sources of existing scheduled transit information; (iv) attitudes and responses to existing fixed schedule information; (v) attitudes and preferences towards real-time transit information; and (vi) the bus frequency for which transit riders would consider the provision of real-time transit information to be necessary. Responses related to attitudes were recorded on a four-point ranking scale (1 4), where 1 meant extremely dissatisfied and 4 meant extremely satisfied. For Calgary s population, sample size calculations indicated a survey size of 386 with a 5% margin of error at the significant alpha level of The survey was conducted at several randomly selected major bus stops in Calgary from 21 June to 5 July 2010; and it was carried out for two shifts on each day (from 07:00 h to 11:00 h and from 12:00 h to 18:00 h). During the field survey, a safe place around a bus stop was chosen. A surveyor then approached the nearest individuals to ask if they would fill out the survey. The respondents who agreed to participate in the survey were asked to fill out a questionnaire. Participants were informed that they could withdraw at any time and that they could complete the survey even after boarding a bus, if their bus arrived before they completed the questionnaire. In such a situation, a surveyor would accompany the participant to collect the completed questionnaire. The survey was, designed bearing in mind, that the objective and scope of this study was the determination of transit users perceptions about the quality of current bus information and users views and preferences regarding a real-time bus information system. In this context, it should be noted that Calgary s winter is long and quite cold. Temperatures in the range of 10 Cto 25 C are common. The snow normally starts in mid to late November and lasts until late March. Winter peaks during the months of January and February. The sample demographics and descriptive statistics are presented in the results section. Analyses of variance (ANOVAs) were conducted to examine significant differences from the mean score of the mentioned choices for some questions. The number of points on rating scales varies widely, and there appears to be no standard [54]. For example, rating scales used to measure public approval of the US President s job performance vary from 2 to 5 points [55]. However, offering a midpoint on a scale (i.e. scales with an odd number of points) may constitute a cue encouraging satisfaction (i.e. people low in ability and/or motivation select the middle or neutral choice for expediency) [56]. Thus, a 4-point Likert scale was used in this study, in order to provide clearer statistical outcomes, because neutral responses are not statistically helpful ([54,57]).

5 340 M. M. RAHMAN ET AL. The willingness to use real-time information before and after starting a trip was assessed by the aforementioned 4-point Likert scale, where 1 represented rarely and 4 almost always. To examine the effects of different variables of interest (e.g. gender, age, frequently used transport mode, availability of alternative modes, access time from origin to the nearest stop and current transit trips per week) on the willingness-to-use score, ordinal regression models were performed justified by the fact that our dependent variable is ordinal in nature [58 61]. When the response variable is a binary or dichotomous variable, binary logistic regression is a suitable technique, because it was developed to predict a binary dependent variable as a function of predictor variables [59,62]. Thus, binary logit models were also calibrated by regressing the willingness-to-pay score (i.e. willing was 1 and not-willing was 0), and the willingness-to-make-more-transit-trips score with similar scores, for the possible explanatory variables. 5. RESULTS 5.1. The sample demographics and other travel characteristics Table I provides the descriptive statistics. Forty-eight percent of the respondents were male. The majority of the respondents (63%) were between 25 and 44 years old, but a considerable number of the respondents (30%) were under 25 years of age. These results are similar to the findings of a survey performed by Calgary Transit [43]. In the questionnaire, the respondents were asked to indicate their most frequently used transport mode. This question captured the main mode of transport of the respondents, regardless of trip purpose, trip time or the feeder mode used. For instance, in this study, the term LRT users refer to the respondents who use LRT most frequently compared with other modes of transport. However, they may use their own vehicles or feeder buses to access LRT stations. Some of them may use bicycles in the summer instead of LRT. When a respondent stated that he/she drove a car to complete a trip, the travel mode of transport was noted as car (drive); whereas, for a respondent who stated that he/she completes a trip by riding as a passenger in a car, the travel mode was denoted as car (ride). The results for the main mode of transport used showed that a high percentage of the respondents use public transport, with 46% and 34% using bus and LRT, respectively. Table I does show that about 18% of the respondents used private cars as their main mode of travel. This low share of private car as the main transport mode can be explained by the fact that the survey targeted transit users waiting at bus stops. Moreover, respondents were asked to indicate whether they had an alternative mode to make their trip other than transit. Thirty-seven percent of the respondents stated that they did not. This group is labelled in this study as captive users. Respondents reported walking for an average of 5.5 minutes to get to the nearest public transit stop/station from their home. They reported making an average of 8 trips per week by transit. Table I. Demographics and travel characteristics of the sample. Variables Frequency (%) Variables Frequency (%) Gender Male 47.9 Under Female Main mode of travel Bus C-train Car (drive) 15.8 Captive user Car (ride) 2.3 Yes 36.5 Cycle 0.6 No 60.4 Walking 1 No opinion 3.1 Current trips per week Access time from home to the nearest stop Cont. Variable, SD = 1.93, Mean = 7.88 Cont. Variable, SD = 2.7 m, Mean = 5.5 m Age

6 USERS VIEWS ON CURRENT AND FUTURE REAL-TIME BUS INFORMATION SYSTEMS Attitudes towards current transit information Respondents were asked if the current scheduled transit information provision system discouraged them from using public transport. Forty percent of respondents stated that they either disagreed or strongly disagreed with this statement, whereas 36% either agreed or strongly agreed. Given that 36% is a sizeable proportion of respondents, it is likely that improved information quality may attract more passengers Boarding longer bus routes If several routes were acceptable to respondents, they were asked if they board the first bus that comes along, even though it may take longer to complete the trip, as they did not have real-time next bus arrival information for the alternative bus route with the shortest travel time. Eighty-two percent of respondents stated that they either agreed or strongly agreed with this statement. These responses point to the fact that providing real-time information can reduce in-vehicle passenger travel time, in addition to the waiting time at the stop Cut-off headway for providing real-time information Respondents were asked to indicate the headway beyond which real-time bus information is appropriate. Figure 1 shows the distribution of passengers responses: 44% of the respondents stated that real-time information was appropriate when the headway was longer than 15 minutes, 20% when headway was longer than 20 minutes and about 14% indicated a headway of 10 minutes. From the cumulative distribution of the response, it is clear that about 88% of respondents thought that real-time information was not necessary if the headway was less than 10 minutes Preferred media Respondents were asked to rank their preference for four different media used for the dissemination of real-time bus information: the Calgary Transit website, a call centre, SMS or PDA applications, and display boards at bus stops. A 4-point scale was used, where 4 represented the highest preference. Table II summarizes the results and shows that website and call centre had higher preference for pre-trip information. Table II also shows that a display board at a bus stop had the highest preference for en-route information. The ANOVA was used to test the hypothesis that the mean score of the preferred media (i.e. namely transit website, call centre, SMS or PDA applications, and display board at bus stop) among different media for pre-trip and en-route information were not significantly different. The results are shown in Table II. Figure 1. Appropriate bus headway for real-time information.

7 342 M. M. RAHMAN ET AL. Table II. Media preference to get real-time information. Trip stage Media to get info Mean score Variance Significance F p-value F crit Pre-trip Information Browsing website < Call centre SMS PDA/Smart phone apps En-route information Display board at bus stop < Call centre SMS PDA/Smart phone apps SMS, short message service; PDA, personal digital assistant. From Table II, it can be observed that the F-value was 525.4, which was greater than the F critical -value (2.61), and that the p-value was less than for the media preference for pre-trip information. With the media preference for en-route information, the F-value and F critical -value were and 2.61, respectively, whereas the p-value was less than Hence, the null hypothesis of equal means for both cases was rejected, which reflects that the difference in media preferences for pre-trip and en-route real-time information was statistically significant. These findings highlight the importance of disseminating the transit information through various media to meet the needs of different riders with various preferences Real-time information content Figure 2 summarizes the respondents ratings for different real-time transit information content. The results show that, from the respondents perspective, the more valuable information was the estimated time of the next bus arrival, followed by news on disruptions. The lowest level of importance was attributed to the map showing real-time bus location. For a future trip, the majority of respondents rated three characteristics earliest possible arrival/ departure time, latest possible arrival/departure time and mean bus arrival time as either important or very important. Nevertheless, 87% of respondents indicated that the earliest possible arrival/departure time was either important or very important. The results of the ANOVA test, as presented in Table III, indicate that the F-value, F critical -value and p-value were , 3.00 and <0.001, respectively. For the preference for different types of information for a future trip, the F-value, F critical -value and p-value were 11.76, 3.00 and <0.001, respectively. Hence, the null hypothesis of equal means for both cases was rejected, which indicates a significant difference in Figure 2. Importance ratings of different types of information content.

8 USERS VIEWS ON CURRENT AND FUTURE REAL-TIME BUS INFORMATION SYSTEMS 343 Table III. Preference for different type of information. Time of browse Media to get info Mean score Variance Significance F p-value F crit For next bus Real-time next bus arrival time < Real-time location map Real-time disruption news For future trips Earliest possible arrival time < Latest possible arrival time Mean bus arrival time preferences among these different types of information and that the next bus arrival time and earliest possible arrival time were the most important type of information to be provided Different trip purposes Respondents were also asked to score their trip purposes on a 4-point scale, in terms of importance of acquiring real-time bus information. The results are summarized in Table IV. Work/school trips had the highest average score (3.58), whereas social/recreational trips obtained the lowest score (1.83). It is interesting to note that shopping trips received a higher score than medical/dental trips. One reason may be a fewer number of such trips are served by public transit, because people may prefer other transport modes, for example, their own vehicle or taxi, when making medical trips. Table IV also includes the results of the ANOVA test. It can be observed that the F-value, F critical -value and p-value were , 2.61 and <0.001, respectively, for the preference of real-time information in relation to the type of trip. Hence, the null hypothesis of equal means was rejected, which indicates a significant difference in the preference for real-time information among the different trip purposes. These findings may be explained by the fact that work/school-related trips can be more stressful and time constrained than other trips. Thus, to avoid delays, work/school-related trip makers are more prone to seek information Acceptable error margin The reported maximum acceptable error in estimating the real-time bus arrival time as perceived by the respondents is illustrated in Figure 3. The acceptable estimation error of one minute (i.e. the bus arrives a minute earlier than the estimated arrival time) did not change, regardless of the bus arrival time (i.e. whether the bus is coming within 10 minutes or within 30 minutes). On the other hand, the range of acceptable error that leads to a higher waiting time at a bus stop (i.e. the bus arrives later than the estimated arrival time) increased with increased time until the bus arrival. This means that respondents were prepared to accept higher error when the bus was expected within 30 minutes than within 10 minutes. An ANOVA test was conducted, and it was observed that the F-value, F critical -value and p-value were 82.36, 3.00 and <0.001, respectively, for a mean acceptable estimation error for late arrival buses. Hence, the null hypothesis of equal acceptable error margins for different expected bus arrival Table IV. Preference for type of trip to have real-time information. Trip purpose Mean score Variance Significance F p-value F crit Work/school trip < Shopping trip Medical/dental trip Social/recreational trip

9 344 M. M. RAHMAN ET AL. Figure 3. Acceptable error margin in estimation of real-time info. times for late arrival buses was rejected, which indicates that users were ready to accept different levels of error margin in the estimation of late arrival times. However, for early arrival times (i.e. the bus arrives a minute earlier than the estimated arrival time), it was found that the F-value, F critical -value and p-value were 0.47, 3.00 and 0.62, respectively. Thus, there was not sufficient evidence to reject the null hypothesis of equal acceptable estimation error for all types of early arrival buses. In other words, users had the same attitude towards early arrival buses, regardless of the arrival time Likely transit ridership increase Respondents were also asked if they would use transit more frequently if they were provided with real-time transit information. They were offered five options as to how much more often they would use public transport, namely 50% much more often, 25% more often, 10% more often, somewhat more often and no change. Table V details the results from among bus, LRT, car and other mode users. The result show that 54% of respondents indicated that there would be no change in their use of public transit, whereas 46% indicated various levels of increased usage. It should be noted that the respondents who stated that they would be using the transit service more often were mainly those who were using either a private car or transit as their main mode. Only a few LRT users indicated their willingness to use transit more often with the availability of real-time transit information. This is expected, because the LRT service is quite frequent during the peak periods and those riders do not see the value of real-time transit information. The response variable (willingness to make more transit trips) was categorized into two groups, namely: (i) those willing to make more transit trips because of the availability of real-time information and (ii) those not willing. This categorization converted the response variable into a binary or dichotomous variable. The binary logistic regression model is a suitable technique to use here, because it was developed to predict a binary dependent variable as a function of predictor variables. Therefore, a binary logit model was calibrated in SPSS statistical software (IBM Corporation, Armonk, NY, USA) [59,62] to further examine the contributing factors that would affect the likely increase in transit ridership with the anticipated availability of real-time transit information. In this model, the Table V. Increased use of public transit. Mode Much more often 50% More often 25% More often 10% Somewhat more often No change Bus 3% 5% 9% 9% 21% LRT 1% 2% 3% 1% 27% Car 1% 2% 7% 2% 4% Others 0% 1% 1% 0% 2% Total 5% 9% 20% 12% 54%

10 USERS VIEWS ON CURRENT AND FUTURE REAL-TIME BUS INFORMATION SYSTEMS 345 logit is the natural logarithm of the odds or the likelihood ratio that the dependent variable is 1 (willing to make more transit trips) as opposed to 0 (not willing to make more transit trips). The probability, p, of a using transit more often is given by p Y ¼ ln ¼ bx (1) 1 p where b is a vector of parameters to be estimated, and X is a vector of independent variables. The definition, mean, standard deviation and count of all of the variables used in this study are reported in Table VI. The estimation results for the binary logic model are reported in Table VII. In SPSS software, the binary logistic regression procedure reports the Hosmer Lemeshow goodness-of-fit statistic to determine whether the developed model reasonably approximates the data. The Hosmer Lemeshow statistic indicates apoorfit if the significance value is less than The results of the regression indicated that the model adequately fitted the data, because the significance value for Hosmer Lemeshow test was It should be noted that, in this study, the log-likelihood for all the calibrated models with the full vector of parameters were closer to zero than the log-likelihood for those models with intercept only, which implies that it was worth having these models. Variables with low statistical significance may also be retained in the model if they belong to factors that have a significant effect on the likelihood of making more transit trips. This approach was adopted for ease of comparison and interpretation of the estimates. Compared with the age group of years, users aged below 44 years were generally shown to be more willing to increase their transit trips with the Table VI. Descriptions of explanatory variables. Explanatory variables Description of variables Mean Standard deviation Count Gender Female 1 = female; otherwise = Male 1 = male; otherwise = Age (years) <25 1 = age < 25; otherwise = = 25 age 34; otherwise = = 35 age 44; otherwise = = 45 age 54; otherwise = = 55 age; otherwise = Travel mode Bus 1 = main travel mode is bus; otherwise = LRT 1 = main travel mode is LRT; otherwise = Car 1 = main travel mode is car; otherwise = Others 1 = other travel modes; otherwise = Captive transit user Yes 1 = captive transit user; otherwise = No 1 = not captive user; otherwise = No opinion 1 = no opinion; otherwise = Existing information quality discourage from using transit Agree/strongly agree 1 = agree/strongly agree; otherwise = Disagree/strongly disagree 1 = disagree/strongly disagree; otherwise = No opinion 1 = no opinion; otherwise = Currently use transit website/teleride Yes 1 = currently use transit website/ teleride; otherwise = 0 No 1 = currently do not use transit website/ teleride; otherwise = 0 No opinion 1 = no opinion; otherwise = Current trips by transit per week Continuous variable Access time (minutes) from home to the nearest stop Continuous variable

11 346 M. M. RAHMAN ET AL. Table VII. Estimation results on willingness to make more transit trips. Variables Coefficient Odds ratio p-value Age (relative to age group 45 54) < Travel mode (relative to bus) LRT <0.01 Car <0.01 Others Captive users (relative to no) Yes <0.01 No opinion Current trips by transit per week Continuous variable Constant Test of model fit (Hosmer Lemeshow test) Chi-square =6.9, df = Nagelkerke R 2 = 0.27, log-likelihood (intercept only) = , log-likelihood (final) = presence of real-time transit information. The results were statistically significant (p < 0.05). Holding other factors constant, users aged under 25 years were 5.05 times more likely to increase their transit trips with the presence of real-time transit information (Odd Ratio [OR] = 5.05), when compared with users in the age group. Users aged were about 4 times more likely to increase their transit trips because of real-time information when compared with users in the age group. Compared with bus users, car users were found to be 4.3 times more likely to make more transit trips with the presence of real-time transit information, which was found to be statistically significant (p < 0.01). On the other hand, LRT users were found to 64% less likely (OR = 0.36) to make more transit trips with the availability of real-time transit information. This was expected, because the LRT is a frequent service that makes the provision of real-time information not as necessary. Both findings were statistically significant (p < 0.01). Additionally, compared with non-captive users, captive users were found to be 2.35 times more likely to increase their use of transit with the presence of real-time transit information (significant with p < 0.01). Compared with frequent transit riders, infrequent transit riders were shown to be more willing to make more trips if real-time transit information was available, for example, a one-unit increase in number of current transit trips caused a decrease of about 5% (OR = 0.95) in the odds of using transit more because of real-time information. However, this was only marginally significant (p =0.10) Likely frequency of using real-time bus information The objective of this analysis was the examination of the factors associated with higher use of real-time transit information. Two stages of using real-time information were examined: (i) pre-trip information and (ii) en-route information. The outcomes from this model should be helpful in identifying the likely frequent users, the appropriate delivery mechanisms of real-time transit information and in designing the message content. The willingness to use real-time information before and after starting a trip was assessed by a 4-point scale, where 1 represented rarely and 4 almost always. Respondents were asked how often they would use real-time information if it were introduced. Pre-trip information is defined as information that is sought before leaving the origin of the trip, whereas en-route information denotes information that is sought after leaving the origin. The results are summarized in Figure 4. Forty-nine percent of the respondents stated that they would use pre-trip information to various degrees, whereas 51% would do so occasionally or rarely. Furthermore, 33% of the respondents stated that they would use en-route information almost always, whereas 58% said they would either use it usually or occasionally.

12 USERS VIEWS ON CURRENT AND FUTURE REAL-TIME BUS INFORMATION SYSTEMS 347 Figure 4. How often respondents use real-time information. The results of the t-test indicate a significant difference in the preference for pre-trip and en-route real-time information, as shown in Table VIII. It is interesting to note that pre-trip information scored lower than en-route information, indicating respondents were more willing to use en-route information if it was available. Caulfield and O Mahony [63] also found a similar result in Dublin, Ireland. This finding is somewhat puzzling, because it is intuitive that pre-trip information should be more important to the riders so that they can make better decisions about when to leave the origin. Some possible reasons may be that the respondents perceived that (i) it helps to reduce anxiety by informing travellers how long they are expected to wait; (ii) it is important to know whether the expected bus has already passed; and (iii) it is easier to get en-route information because it is found on the display boards. This finding requires further study. Because our dependent variable (i.e. willingness to use real-time information) is ordinal in nature, ordinal regression models were calibrated in SPSS statistical software by regressing the score on different variables of interests. The basic form of a generalized linear model is shown in the following equation: link g ij ¼ θj b 1 x i1 þ b 2 x i2 þ...þ b p x ij where g ij is the cumulative probability of the j th category for the i th case, θ j is the threshold for the j th category, p is the number of regression coefficients, x i1...x ip are the values of the predictors for the i th case, b 1...b p are regression coefficients, and link (g ij ) is the link function. In SPSS statistical software, the link function is a transformation of the cumulative probabilities that allows estimation of the ordinal regression model. Five link functions are available to optimize the results, namely logit, complementary log-log, negative log-log, probit and cauchit (inverse cauchy). There is no clear theoretical choice for the link function based on the data. In cases where the initial model performs poorly, it is usually worth trying alternative link functions to see if a better model can be constructed; and in this case, the cauchit link function produced the better model. Table IX summarizes the results. The log-likelihood values for the intercept only model and the final model are shown. The chi-square is the difference between the log-likelihoods of the intercept only model and the final model. The significance level for the chi-square statistic was less than 0.05, indicating that the cauchit model was appropriate. The results also showed that the cauchit model was the only one that did not violate the parallel line assumption. (2) Table VIII. Willingness to use real-time information. Willingness to use Mean score Variance Significance t-stat p-value t-crit Willingness to use pre-trip info < Willingness to use en-route info

13 348 M. M. RAHMAN ET AL. Table IX. Estimation results on willingness to use real-time information. Variables Pre-trip En-route Coefficient p-value Coefficient p-value Gender (relative to male) Female Age (relative to age group 45 54) < Travel mode (relative to bus) LRT Car Others Captive users (relative to no) Yes No opinion Existing information quality discourage from using transit (relative to disagree/strongly disagree) Agree/strongly agree No opinion Currently use transit website/teleride (relative to no) Yes No opinion Current trips by transit per week Continuous variable Access time from home to the nearest stop Continuous variable Threshold Rarely Occasionally Usually Link function Cauchit Cauchit Nagelkerke R 2 = Log-likelihood(intercept only) Log-likelihood (final) Test of parallel lines w 2 = 26.2, df = w 2 = 28.4,df = For both pre-trip and en-route information, there was a significant association between gender and the willingness to use information. Female riders were found to be more interested in both types of real-time transit information. This may be explained by the fact that trip chaining behaviour, as related to childcare (i.e. dropping off children) and household maintenance (e.g. groceries), is more associated with women than men [64]. This may result in women valuing real-time information more, in order to reduce their waiting times. A general downward trend was observed in the potential use of pre-trip information with older age groups. The age group between 25 and 34 years showed higher interest compared with other age groups; however, it was only marginally significant (p = 0.08) for the age group. On the other hand, the age group did not have any statistically significant effect for en-route transit information. The willingness to use real-time information varied significantly by the most frequent travel mode of the respondents. As expected, compared with bus users, LRT users showed lower interest in using either en-route or pre-trip transit information. Compared with bus users, current car users showed a higher interest in using pre-trip information, but lower interest in en-route information. These findings were statistically significant for en-route information (p = 0.01) and marginally significant for pre-trip information (p = 0.07). These findings highlight the need for both types of real-time transit information. Pre-trip transit information may be influential in better trip planning and possibly in convincing frequent car users to choose public transit. On the other hand, en-route information may be more suitable for current transit users, but not for car users who may have already checked for pre-trip transit information.

14 USERS VIEWS ON CURRENT AND FUTURE REAL-TIME BUS INFORMATION SYSTEMS 349 As expected, captive users were more interested in both types of real-time transit information. These findings were found to be statistically significant for both types of real-time transit information (both p = 0.01). Furthermore, respondents who perceived the existing information quality as unreliable were more interested in real-time information. Respondents who were already familiar with either web browsing or using Calgary Transit s Teleride system were more interested in pre-trip information compared with respondents who were not familiar with the two systems. Finally, the results indicate that infrequent transit users were more interested in both types of real-time transit information than frequent transit users. These findings were significant for both types of real-time transit information (p = 0.01). The results also show that respondents who usually walk longer to reach the bus stop were more willing to use real-time information compared with their counterparts. This finding was only statistically significant for en-route information (p = 0.03) Willingness to pay for real-time information Respondents were asked if they were willing to pay for obtaining real-time transit information. The options given were in the form of agree, disagree or have no opinion regarding additional payment for accessing real-time information. Respondents who stated that they agreed to pay for this service were further asked how much more they would be willing to pay compared with the current transit fare (i.e. 20, 15, 10, 5 or 1% more). The results show that 27% of respondents agreed that they would be willing to pay for a real-time information system; however, 54% of respondents were not willing to pay extra for this service. These findings support earlier studies conducted in San Francisco that found that respondents were quite sensitive to an increase in the fare price for the sake of obtaining real-time transit information [65]. This can be attributed to the fact that the majority of the riders expect it to be a free service. On the other hand, 21% of the respondents stated that they were ready to pay 10% more. Among the respondents who stated that they agreed to pay, 36% and 43% were also ready to pay 5% and 1% more, respectively. A binary logit model was calibrated in SPSS software by regressing the willingness-to-pay score (i.e. willing was 1, and not-willing was 0) on different variables of interest. Table X summarizes the results. These results show that female respondents were 1.86 times more likely (OR = 1.86) to pay for Table X. Estimation results on willingness to pay. Variables Coefficient Odds ratio p-value Gender (relative to male) Female Age (relative to age group 45 54) < Travel mode (relative to bus) LRT Car Others Captive users (relative to no) Yes No opinion Existing information quality discourage from using transit (relative to disagree/strongly disagree) Agree/strongly agree No opinion Current trips by transit per week Continuous variable Constant Test of model fit(hosmer Lemeshow test) 0.63 Nagelkerke R Log-likelihood(intercept only) = , log likelihood (final) =

15 350 M. M. RAHMAN ET AL. real-time information than male respondents. When considering the effect of age, riders aged years were shown to be 7.66 times more willing to pay for this service compared with the riders in the age group. As for the mode of transport, compared with bus users, LRT users were 60% less (OR = 0.40) likely to pay for information, whereas car users were 1.41 times more likely to pay (OR = 1.41) for this service. Additionally, captive users were found to be 81% less interested in paying (OR = 0.19) than their counterparts. These findings are intuitive as captive users are mainly associated with specific socioeconomic characteristics, such as being low income, elderly or children [40]. In particular, low-income groups usually associate lower values with their travel time. Additionally, compared with frequent transit riders, infrequent transit riders were shown to be more willing to pay for real-time transit information, for example, a one-unit increase in number of current transit trips cause a decrease of about 9% (OR = 0.91) in the odds of willing to pay for real-time information. Finally, the results showed that respondents who perceive the existing information quality as unreliable were 2.10 times more willing to pay for real-time information when compared with others. 6. CONCLUSIONS AND RECOMMENDATIONS This study has investigated the users views and perceptions towards possible future availability of real-time bus information systems in Calgary, Canada, because the development of such systems should proceed mainly from the perspective of the users, in addition to that of the provider, to ensure maximum benefit from such systems. It appears from the review of the literature and our study that the most preferred media, information content and perceived and measured benefits of real-time information systems varied from city to city. No studies have been previously conducted to estimate the threshold value of bus headway below which real-time bus information will no longer be important as perceived by passengers. The results from this study indicate that real-time bus information should be implemented on bus routes where the bus headway is longer than 10 minutes. In addition, no studies have examined the error margins people are willing to accept from a real-time information system. Our survey results showed that the acceptable estimation error of 1 minute for early bus arrival (i.e. the bus arrives a minute earlier than the estimated arrival time) in a real-time information system did not change regardless of the interval between bus arrivals. On the other hand, the range of acceptable error for the late arrival of buses (i.e. the bus arrives later than the estimated arrival time) increased with time elapsed until the arrival of the bus. Another interesting finding from this study is that real-time bus information can reduce the total travel time (in-vehicle travel time plus waiting time at the bus stop) because a majority (82%) of respondents stated that they usually board the first bus to arrive, due to the uncertainty of the arrival time of the bus for the shortest route, even though the decision may mean a longer in-vehicle travel time. Our results showed that real-time information was perceived to be relatively more beneficial for work/school trips in comparison with social/recreational trips. Interestingly, shopping trips received a higher score than medical/dental trips. One plausible reason is that people prefer other transport modes (e.g. their own vehicle or taxi) when making medical trips. This finding is especially useful for prioritizing routes for implementing real-time information systems. It was observed that display boards at stops were the most preferred medium to get en-route information; whereas, website and call centre were the preferred media for pre-trip information. However, the results also indicated that providing information through as many media as possible promotes information dissemination to the widest group of customers as well as serves populations with diverse preferences. It was observed that 36% of respondents stated that the quality of the current information discouraged them from using buses. As for the preferred information content, next bus arrival time information received the highest priority. It was also confirmed that provision of real-time information would increase the attractiveness of buses. However, respondents were quite sensitive to an increase in fare for the sake of obtaining real-time bus information.

16 USERS VIEWS ON CURRENT AND FUTURE REAL-TIME BUS INFORMATION SYSTEMS 351 In general, LRT users showed the least interest in real-time information. Women, younger riders, current car users and infrequent transit users showed the highest interest. One possible reason may be that current car users and infrequent transit users were not very familiar with transit service parameters and, therefore, felt a need for more accurate information that can be obtained from a real-time system. Perhaps, younger people showed more interest because of their greater familiarity with new information technologies. The interest by women in real-time information may be because of such information being perceived as more convenient and secure. One of the limitations of the study is the reliance on a stated preference survey. As Calgary Transit is planning on deploying a real-time bus information system in the near future, revealed preference information will become available. Accordingly, it is important to validate the results of the models developed in this paper with the data that will be obtained after implementation of real-time information systems on a few routes. ACKNOWLEDGEMENTS This research was supported in part by the PUTRUM Research Program funded by Calgary Transit and by a NSERC Discovery Grant. We wish to acknowledge the support given by Fred Wong, Doug Morgan, Neil McKendrick and other staff at Calgary Transit. REFERENCES 1. Dziekan K, Vermeulen A. Psychological effects of and design preferences for real-time information displays. Journal of Public Transportation 2006; 9(1): Edison WK, Brian F, Alan B, Rutherford GS, Layton D. Where is my bus? Impact of mobile real-time information on the perceived and actual wait time of transit riders. Transportation Research Part A-Policy And Practice 2011; 45(8): DOI: /j.tra El-Geneidy AM, Horning J, Krizek KJ. Analyzing transit service reliability using detailed data from automatic vehicular locator systems. Journal of Advanced Transportation 2011; 45: Pangilinan C, Wilson N, Moore A. Bus supervision deployment strategies and use of real-time automatic vehicle location for improved bus service reliability. Transportation Research Record: Journal of the Transportation Research Board 2008; 2063(1): Chen M, Yaw J, Chien SI, Liu X. Using automatic passenger counter data in bus arrival time prediction. Journal of Advanced Transportation 2007; 41(3): Yu B, Lam WHK, Tam ML. Bus arrival time prediction at bus stop with multiple routes. Transportation Research Part C - Emerging Technologies 2011; 19(6): DOI: /J.Trc Chang H, Park D, Lee S, Lee H, Baek S. Dynamic multi-interval bus travel time prediction using bus transit data. Transportmetrica 2010; 6(1): DOI: / Khan AM. Bayesian predictive travel time methodology for advanced traveller information system. Journal of Advanced Transportation 2012; 46: Shen L, Hadi M. Practical approach for travel time estimation from point traffic detector data. Journal of Advanced Transportation Yu B, Yang Z-Z, Chen K, Yu B. Hybrid model for prediction of bus arrival times at next station. Journal of Advanced Transportation 2010; 44: Kim S, Lee C, Kim Y, Lee S, Park D. Error correction of arrival time prediction in real time bus information system. Journal of Advanced Transportation 2010; 44: Chien SI-J, Daripally SK, Kim K. Development of a probabilistic model to optimize disseminated real-time bus arrival information for pre-trip passengers. Journal of Advanced Transportation 2007; 41: DOI: /atr Schweiger CL. Real-time bus arrival information systems. Transit Cooperative Research Program (TCRP) Synthesis Report 48, published by Transportation Research Board, Washington, Federal Highway Administration. Traffic congestion and reliability: Linking solutions to problems, Cambridge Systematics, Inc: Washington, D.C., Taylor BD, Fink CNY. The factors influencing transit ridership: a review and analysis of the ridership literature. University of California Transportation Center, Litman T. Valuing transit service quality improvements. Journal of Public Transportation 2008; 11(2): Abdel-Aty MA, Jovanis PP. The effect of ITS on transit ridership. ITS Quarterly 1995; 3(2): Abdel-Aty MA. Using ordered probit modeling to study the effect of ATIS on transit ridership. Transportation Research Part C: Emerging Technologies 2001; 9(4):

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19 354 M. M. RAHMAN ET AL. APPENDIX A. QUESTIONNAIRE OF THE SURVEY.