A model to estimate the marginal walking time of bus users by using adaptive neuro-fuzzy inference system

Abstract

As bus users give up riding buses and choose the other alternatives when walking times to bus stops are longer than their marginal walking time, whether the bus stops are available within the marginal walking time at origin and destination of their travel affects definitely to bus users' bus service selection. Therefore, bus service coverage area should be decided considering reasonable walking distance or time from bus stops. In this study, an ANFIS model to estimate the marginal walking time was constructed by empolying the variables of age, income and present walking time which have fuzzy characteristics among individual attributes of bus users. Predictability of the model was evaluated by comparing the surveyed and the estimated values. Based on the statistics of coefficient of determination, MSE and MAE, it may be judged that the explainability of the model is very high, the values of the statistics being 0.996, 0.163, 0.333 respectively. The marginal walking time estimated by inputting the average values of age and income of Jinju City bus users to the model was close to the mean of the surveyed. The technique developed in this study can be applied to other cities. Analyzing the relationship between marginal walking time and age/income by using the model, the marginal walking time decreases slowly after the age of 28 years and is inversely proportional to the income.

To What Extent May Transit Stop Spacing Be Increased before Driving Away Riders? Referring to Evidence of the 2017 NHTS in the United States

“…Thus, it is indicated that transit agencies in the United States may accordingly enlarge stop spacing as advocated in the existing research [9,16], where acceptable stop spacing for the rail service is largest, while that for the R-A and NR-A bus service is similar. Thus, this research adds to the literature with an exploration of the vertex of acceptable transit stop spacing based on the inference from the observable transit walk time, instead of empirical statistics [9,10], optimization analysis [20], or a revealed preference survey [27][28][29][30]. Moreover, the result extends previous literature with categorized transit service, finding that the NR-A bus service has the largest gap between the actual stop walk time and its vertex, while the R-A bus service has the smallest.…”

“…Recently, delicate research has been implemented to account for the effect of passenger characteristics and service quality on transit stop spacing. With respect to passenger socio-economics, Kim et al [27] conducted a questionnaire to obtain passengers' sociodemographics and transit use information in various city types, including the factors of gender, age, employment status, income, car ownership, transit use frequency, trip purpose, and access mode [28]. With cross-sectional statistics and adaptive neuro-fuzzy inference model, it was found that age and income significantly influenced the maximum acceptable walk time to transit stops.…”

“…For both the rail and bus service, the coefficients of the natural logarithm of household density and household income are always significantly negative, albeit to a slight extent. For example, when travelers' household density increases by 1%, stop walk time vertex can be reduced by 0.03%, 0.04%, and 0.05% for the R-A rail, R-A bus, and NR-A bus services, respectively; when household income increases by 1%, walk time vertex can be Thus, this research adds to the literature with an exploration of the vertex of acceptable transit stop spacing based on the inference from the observable transit walk time, instead of empirical statistics [9,10], optimization analysis [20], or a revealed preference survey [27][28][29][30]. Moreover, the result extends previous literature with categorized transit service, finding that the NR-A bus service has the largest gap between the actual stop walk time and its vertex, while the R-A bus service has the smallest.…”