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The main findings are that a vast majority of the population is not attached to the car, but ready to change to PT, if its service includes direct and seated transport – these are hardly characteristics satisfiable with conventional collective transport. Instead, radical technological innovations such as PRT have the potential to satisfy all characteristics required to trigger a significant mode-shift to a sustainable public transport mode.

The current study investigates how employing traffic counters at best to count bike flows. In particular, this paper illustrates the main characteristics of traffic counters, discussing their advantages and shortfalls, and where they have to be located in order to minimize vehicular and pedestrian interferences when counting bike flows. Instruments were placed in 20 sections, for 7 days. Data were collected continuously. The choice of the particular type of counter, either radar or pneumatic, was performed by taking into account both geometrical and operational variables such as bicycle lane width, type of transport mode allowed, and proximity to roads with high traffic volumes. In addition to instrumental bike flow monitoring, a manual monitoring was conducted in each section. Operators’ reports indicate the instant in which bike cross the monitored section, the direction, and the number of cyclists riding outside the bicycle paths. Manual monitoring is useful to compare data, to determine the level of use of cycle tracks, to calibrate instruments and evaluate their precision. Instrumental data consist of numerical strings containing date, time, direction, speed and number of reflection points, which indicate vehicle size. During the instrumental monitoring phase, a first calibration was performed, determining a range of reflection points corresponding to each class of vehicle. In this way we could distinguish between pedestrian, bicycles, scooters, and cars. Secondly, instrumental data were compared with manual data referred to the same time interval in order to improve the results obtained in the first calibration step. Indeed, under the assumption that the error associated to manual data is lower with respect to the instrumental data one, the number of events recorded by instruments is similar to the number of events recorded by operators. In our case, the error regarding instrumental counts was about 10%. For the specific case of radar traffic counters, the number of reflection points changes according to the direction of travel, due to the value of tilt angle, measured with reference to roadway axis. Then, the collected data were introduced in templates, that allowed drawing bar-graphs with the number of bikes every 15-minute interval, during every day of measurement. By processing these data, we obtained daily flows, weekday and holiday average flows, medium frequency, peak hour factor, level of use, peak periods, climatic dependence and other information. The methodology applied in this research is easy to use and can be adapted to the specific goal of the study that will be performed in order to individuate critical situations and possible interventions.

In this paper, the results of a research regarding the transit network of the city of Bologna are presented in order to highlight those factors affecting the reliability of a transit service at high frequency. In literature, several authors relate bus service reliability to the variability of travel time performance (Polus, 1978; Sterman and Schofer, 1976); others with the adherence to schedules (Bates et al., 2001), others to the maintenance of the designed service frequency (Janos and Furth, 2002), others with the waiting time at the bus stops (Bowman and Turnquist, 1981), and others to the impossibility of getting on the bus because of capacity issues (Chapman, 1976). Anyway, the reliability of a transit service is a key element both for users - real and potential ones - and providers. Low service reliability implies the reduction of the service quality perceived by users causing an increase of the amount of time passengers must wait at a bus stop as well as the comfort reduction due to vehicles’ overcrowding (Bates et al., 2001; Rietvield et al., 2001; TCRP, 1999; Prioni and Hensher, 2000; Welding, 1957; Turnquist, 1978). From providers perspective, low service reliability means a reduction of the veh-km delivered and an inefficient utilization of the fleet: some buses travels full of passengers whereas others are empty. This implies the need of additional vehicles to be provided, thus increasing the operating costs. Moreover, as reliability decrease, public transport demand will decrease too thus engendering a lower income for the service provider. The reliability is strictly related to the bunching phenomenon. Bus bunching refers to a group of two or more transit vehicles scheduled to be evenly spaced along the same route, which run in the same location at the same time. This occurs when at least one of the vehicles is unable to keep its schedule because of the interaction with other vehicles of the traffic steam, presence of signalized intersections along the route, demand increase, etc., which implies a bus to be early/late with respect to the planned schedule. Theoretically, the driver should try to adjust the bus speed in relation to the gap with the preceding bus; although it represents one of the most important hypothesis of bunching simulation models, this procedure, which requires driver knows the position of the preceding vehicles, is not usually adopted (Nagatani, 2001). This research provides a general analysis based on reliability, identifying and discussing its determinants. The relative importance of each determinant is analyzed through a survey conducted along two of the main bus routes of the Bologna transit network. This analysis could allow providers identifying appropriate strategies, preventive or corrective ones, in order to improve the service reliability.

This research is mainly focused on the aspect of the external costs of transport, by conducting a case study at the city of Rimini, a popular seaside resort at the Italian North-Adriatic coast. In particular, Rimini’s current urban traffic scenarios have been compared with an alternative mobility pattern based on a new PRT network, which would be intrinsically safe and environmental friendly. The results of the micro-simulations performed demonstrate that the energy-efficient and emission-free PRT vehicles can provide significant advantages in terms of external cost savings. Indeed, compared to the current situation, a reduction by 53% in primary energy consumption and by 69% in CO2 emissions could be obtained by implementing the proposed PRT network in Rimini.

Una fase di fondamentale importanza nella gestione dei servizi di Traspor-to Pubblico Locale (TPL) è la realizzazione del monitoraggio del servizio, al fine di misurare la qualità del servizio di trasporto collettivo stesso (D.Lgs. 422/97; LR30/98 Emilia Romagna). Tale misura costituisce una fase essenzia-le per la verifica delle specifiche di un contratto di servizio da parte dell’ente locale contraente. A tale scopo possono essere impiegati i seguenti indicatori di qualità del servizio: l’indicatore di regolarità, definito dal rapporto tra il numero di corse rilevate e quelle programmate; l’indicatore di puntualità, a sua volta definito dalla differenza tra l’effettivo orario di passaggio di una corsa e quello programmato, o dalla frequenza dei ritardi superiori a 5 minuti, oppure dalla differenza fra il tempo medio di attesa rilevato ed il tempo medio di attesa da orario. Lo studio, condotto sulla base di dati rilevati sperimental-mente relativi ad alcune linee urbane della rete di trasporto collettivo di Bologna, ha l’obiettivo di analizzare le relazioni fra tali indicatori di qualità del servizio ed alcune variabili caratteristiche delle linee, come per esempio: la percentuale di linea su corsia riservata, la lunghezza media della tratta di linea fra due fermate successive, il tipo veicolo impiegato, il numero di semafori per unità di lunghezza. In particolare l’individuazione di tali relazioni può contribuire a determinare su quali variabili è conveniente intervenire ed in quale misura affinché gli indicatori di regolarità e di puntualità soddisfino i valori di soglia riportati nel contratto di servizio.