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Railroading the rules
Published in Dinesh Mohan, Geetam Tiwari, Sustainable Approaches to Urban Transport, 2019
Thiawalas are intermediary collectors who operate out of a “thia” in markets and other central locations. They do not go to collect the waste but the waste generator comes to them for sale of the waste. Thus, they do not need to use cycle rickshaws but prefer to walk as their personal mode of transport (Table 8.16). In the context of the waste pickers, though, what is of great significance is the correlation between the mode of transport, the loads that can then be transported, and the related earnings. As the survey data shows, the waste pickers who operate on foot are largely able to carry less than 40 kg of waste on one trip, have a range of 6–7 km, and earn Rs 50 daily. Those who have cycles are mostly transporting between 40 to 60 kg over 20–25 km and earn Rs 100 per day. The rickshaw operators load 40–100 kg in one trip, but travel 10–15 km.
Extraction and analysis of microscopic traffic data in disordered heterogeneous traffic conditions
Published in Transportation Letters, 2021
R. B. Amrutsamanvar, B. R. Muthurajan, L. D. Vanajakshi
The aggressive-following nature of the PTWs can be recognized from Figure 12, where a significant increase in the frequency can be seen for PTWs with less than two-meter longitudinal distance. The auto-rickshaws, in general, have limited power to accelerate compared to the other motorized vehicles, which restrict their overtaking maneuvers. Moreover, they stop frequently to pick up the passengers. Therefore, most of the times, these vehicles move at a monotonous speed irrespective of the influence of the leader vehicle.
Modelling injury severity of victims in collisions involving public transit in Dhaka, Bangladesh
Published in International Journal of Crashworthiness, 2023
Bijoy Saha, Mahmudur Rahman Fatmi, Md. Mizanur Rahman
In the study, a number of variables are considered from the crash record and built environment data. In crash record, victim-level information of crash record refers to person profile which includes variables related to age and gender of the victim, road user type, impairment of driver. The road user type refers to the user such as driver and passenger of different travel modes such as private vehicle, PT, truck, motorcycle, biking, walking, and unconventional modes. Unconventional mode includes human-pulled three-wheeler vehicle (i.e. known as rickshaw), motorised three-wheeler taxi (i.e. known as baby-taxi) and motorised three-wheeler paratransit (i.e. known as tempo). Rickshaw is a non-motorised vehicle offering ride to two passengers. The occupancy of baby-taxi vehicles is three passengers, and it is operated as a taxi service in Dhaka. In contrast, the occupancy of tempo is fourteen passengers, and it is operated in fixed-routes service in Dhaka. Crash level attributes comprise of environmental attributes and road network related variables. Among environmental attributes, time of collision (e.g. time of the day, day of the week and month of the year), weather condition (e.g. fair, rain and fog), light (e.g. daylight and dawn) have been considered. Road network related variables include intersection type (e.g. four-way, three-way and roundabout), traffic control system (e.g. signalised, un-signalised and traffic police controlled), movement type (e.g. one-way and two-way), and existence of divider. In case of built environment attributes, transportation infrastructure and land use characteristics have been generated for a 250-m buffer area from each collision location. Transportation infrastructure characteristics include length of sidewalk and railroad, and number of bus terminals, truck terminals and water vehicle stations within the above-mentioned buffer area. Land use type variables include percentage of residential area, commercial area and other type of land usage. Land use variables also include the region of the crash occurrence. Dhaka metropolitan area is under the jurisdiction of 50 police stations, which is used to divide Dhaka City into 50 regions. Crash record identifies the region of the crashes. These regions are also used as independent variables. In Table 1, summary statistics of the variables retaining in the final model has been reported.