Determination minibuses dwell time in Istanbul

The effects of increasing population and travel demand in the development of urban public transport systems are obvious, and the efficient operation of public transportation systems is the solution of possible traffic congestions. Many different public transport modes serve in the cities and it is important that these modes are integrated with each other. A public transport system with efficient and integrated service is possible by the well-defined characterization of public transport modes and their proper applications on site. In this context, a well-designed system from the largest link to the smallest link of the public transport network will contribute greatly to the sustainability of the city. In the developing countries, different solutions have been developed by private operators where the urban public transportation modes are insufficient; thus, the paratransit transportation modes have been created. There are many types of paratransit transportation modes, and this study examines the minibus transportation with significant contributions to the urban public transport system. It is known that the minibus transportation in the developing countries has an important place in the urban public transport system while it competes with other public transportation modes rather than complementing them. On the other hand, in the developed countries, the minibus transportation is a type of transportation used in areas where the bus operation is not economical, and especially in the suburban areas. Therefore, the development status of countries changes the definition and use of minibus transportation. As the minibus transportation uses the existing transportation network with other vehicles, minibuses generally trigger the traffic congestion in the areas where traffic is intense. It is seen that minibus and bus transportations in these regions support each other or compete with each other in a different way. The buses serve passengers with higher capacities and stop at the predefined stops; while the minibuses have more flexible routes and they stop randomly to pick up or drop-off passengers. Obviously the average speed of minibuses is less than the buses' and their effect to present traffic is worse, especially in the areas with heavy traffic. In this study seven different minibus routes were selected in İstanbul and the travel times during peak and off-peak hours, their travel speeds, their number of passengers and their number of stops were determined. In all the minibus routes those are examined, it is determined that during peak hours the average travel time, the average number of passengers and the average number of stops are higher than the off-peak hours' values by 15%, 67% and 44% respectively. The average travel time of all minibus routes is 4.0 min/km, the average travel speed is 15.0 km/h, the average number of passengers is 36 and the average number of stops is 2.7 stop/km. Moreover, the average travel time during peak hours is 4.3 min/km, the average travel speed is 14.0 km/h, the average number of passengers is 45 and the average number of stops is 3.2 stop/km. A number of regression analyses were carried out between the length of routes as the dependent variable and the explanatory variables such as the average number of passengers and the average number of stops with respect to the travel time (peak and off-peak). It was determined that as the route length increases the average number of passengers and the average number of stops increase; however, the number of passengers per km and the number of stops per km decrease. The minibus passengers are stratified according to gender and age groups to examine the characteristics of passengers using the minibus transportation. The age groups were selected as young (<25), adult (25-65), and elderly (65+), respectively. In the sample, 53% of the passengers are male, predominantly adult. In addition, the rate of elderly people among all passengers is only 6%. The crowdedness levels at each stop in minibus routes were recorded and the rate of the presence of the standing passengers were determined as 65% and %30 for peak and off-peak period respectively. In particular, it was seen that 38% in the peak and 11% in the off-peak, the number of standing passengers was more than 5 passengers which increased the passenger alighting time and reduced the passenger safety and comfort. According to the distribution of boarding and alighting passengers, it was determined that single-passenger alighting or boarding cases constitute 57% of total data and the ratio for which there are less than three or less alighted or boarded passengers, is 89%. Evidently, minibuses usually make short-timed stops at the routes which reduces the number of passengers boarding and/or alighting. It is necessary to categorize the stops made by minibuses for passengers alighting and boarding into two main groups. These are the adequate waiting and the excessive waiting. Excessive waiting can be defined as a longer waiting time, where the chance of picking-up passengers is high. Although, the total number of excessive waiting in all minibus routes constitutes 10% of the total, the total duration of the excessive waiting is 50% of the total dwell time during the peak hours and 60% during the off-peak hours. In this context, excessive waiting increases the travel time, creates problems in the minibus frequency and consequently reduces the efficiency of the minibus system. In minibus routes; while the average dwell time in adequate waiting is 4.5 seconds, it is determined that it rises to 47.4 seconds in excessive waiting. Also in adequate waiting, there is a strong relationship between the dwell time and the total number of alighted and boarded passengers. The dwell time linear regression model which created for adequate waiting is 0.36 seconds dead time plus 2.12 seconds for each alighted or boarded passenger. The minibuses usually wait adequately to serve their passengers, and the time spent on the sum of these waiting times constitutes a part of the travel time. In this direction, it is necessary to find the variables that affect the passenger boarding time, the passenger alighting time and the dwell time in order to make the travel time more efficient. In this study, the relation between the passenger boarding time, the passenger alighting time and the dwell time and other variables such as, "location of operation", "travel time", "crowdedness level", "passenger characteristics", and "vehicle characteristics" variables have been examined. Firstly, the effects of the location on which the minibus was operated on the passenger alighting time and the passenger boarding time was investigated and it was found that this variable only affected the passenger alighting time not the passenger boarding time. In the same way, the travel time variable affects only the passenger alighting time. It is thought that this situation is related to the level of crowdedness rather than the location or travel time. The passenger alighting time lengthens when during peak hours and on the routes which have higher inner vehicle crowdedness and thus makes a difference. It is seen that the level of vehicle crowdedness is effective on the minibus dwell time. In general, the increase of crowdedness level increases the average passenger boarding time and the average passenger alighting time. It is determined that passenger gender and age are effective on the dwell time. In this context, passenger boarding time and the passenger alighting time of female passengers is higher than that of male passengers and increase in passenger age increases the passenger boarding time and the passenger alighting time. Two types of minibuses are operated such as staired and no-staired. In the minibuses, the existence of the stairs is not found to be effective on the passenger boarding time and the passenger alighting time. Nowadays, it is known that minibuses are designed no-staired to ease alighting and boarding. Finally, the relation between the passenger boarding time, the passenger alighting time, the dwell time and the dependent variables is modeled with the help of the multivariate regression method and the coefficients of determination of the models are found to be 0.130; 0.246 and 0.735 respectively.