Generation of “Biking Schedules” for Bicycle Travel Analysis

With an increasing focus on bicycling as a mode of urban transportation, there is a pressing need for improved tools for bicycle travel analysis and modeling. This paper introduces “biking schedules” to represent archetypal urban cycling dynamics, analogous to driving schedules used in vehicle emissions analysis. Three different methods of constructing biking schedules with both speed and road grade attributes are developed from the driving schedule literature. The methods are applied and compared using a demonstration data set of 55 h of 1-Hz on-road GPS data from three cyclists. Biking schedules are evaluated based on their ability to represent the speed dynamics, power output, and breathing rates of a calibration data set and then validated for different riders. The impact of using coarser 3, 5, and 10 s GPS logging intervals on the accuracy of the schedules is also evaluated. Results indicate that the best biking schedule construction method depends on the volume and resolution of the calibration data set. Overall, the biking schedules successfully represent most of the assessed characteristics of cycling dynamics in the calibration data set (speed, acceleration, grade, power, and breathing) within 5%. Future work will examine the precision of biking schedules constructed from larger data sets in more diverse cycling conditions and explore additional refinements to the construction methods. This research is considered a first step toward adopting biking schedules in bicycle travel analysis and modeling, and potential applications are discussed.

Digital Narratives of Place: Learning About Neighborhood Sense of Place and Travel Through Online Responses

As the market penetration of mobile information and communication technologies continues to grow, visitor feedback, such as online reviews of locations or sites visited, will continue to grow in parallel at finer temporal and geographic scales. This growth in data opens the opportunity for travel demand analysts to assess location attractiveness on the basis of online reviews and subsequently inform destination choice models. In geography and urban planning, the construct of sense of place (SOP) has emerged as an indicator for visitor association or connection with a place or site. An opportunity exists for examining SOP through the lens of text mining (i.e., extracting information from online text reviews and forming digital narratives of place). Several websites devoted to sharing feedback on experiences and overall perceptions exist, including Yelp and TripAdvisor. With text-mining methods, previously unidentified SOP-related topics and issues may emerge from online reviews and serve as a basis for subsequent analysis. The results from this study indicate that these emerging topics or terms require more contextual information and interpretation. As a stand-alone method, text mining is insufficient for identifying SOP topics, given the complexity of dimensions that characterize SOP. In addition, the results suggest that timing and seasonality play an important role in visitors’ evaluation of a site; these factors have received less attention in the literature. With respect to text mining as a methodology to gain insights into SOP and supplement existing travel analysis, several barriers exist, including interpretation of topics from topic models. Nonetheless, these approaches are promising and require more research to guide practical implementation for inferring SOP from online text reviews and integration with existing travel analysis approaches.

THE INFLUENCE SERVICES QUALITY, PROMOTION AND PHYSICAL EVIDENCE AGAINST CUSTOMER LOYALTY CV. FUNTASTIC TOUR AND TRAVEL

The purpose of this study is to find the influence of quality services, promotion and physical evidence of customer loyalty on CV. Funtastic Tour and Travel. Analysis method of research used quantitative analysis to compute multiple linear regressions by using the Statistical Program for Social Science (SPSS) 20 conducted on customers CV. Funtastic Tour and Travel. The results of this study indicated that the independent variable (quality of services, promotion and physical evidence were able to explain the variations that occurred on customer loyalty by 60, 2%. And explained that the variable of quality service did not affect the customer loyalty with a regression coefficient - 0,114, while the promotion variable affected positively to the customer loyalty with regression coefficients 0,274 and most dominant variable influenced positively customer loyalty is variable physical evidence of regression coefficients 0,588. Key word: service quality, promotion, physical evidence and customer loyalty