Service reliability, which is a result of variability in operations (Abkowitz and Engelstein 1983; Ceder 2007; Mazloumi et al. 2010), is the certainty of service aspects (such as travel time) compared to the schedule as perceived by the user (Furth and Muller 2009; Van Oort et al. 2012). Unreliability causes longer and uncertain passenger journeys (Bates et al. 2001; Noland and Small 1995; Van Oort et al. 2012). The level of service reliability affects several choices made by travellers, such as mode and route (Schmöcker and Bell 2002; Turnquist and Bowman 1980). In numerous studies, reliability-related attributes have been found among the most important service attributes in a variety of situations. Balcombe et al. (2004) even report that passengers consider service reliability twice as important as frequency. König and Axhausen (2002) conclude that research over the last decade shows that the reliability of the transportation system is a decisive factor in the choice behaviour of people.
Even though reliability is thus considered of key importance, in Van Oort et al. (2012) it is demonstrated that passengers presently experience an insufficient level of service in this field. It is revealed that service reliability impacts on passengers depend on three main aspects, as shown by Fig. 1:
How does the vehicle operate (compared to the schedule)?
How do passengers travel through the network?
How do passengers behave (concerning arrival at the first stop and transferring)?
Much scientific research and public discussion is being conducted concerning the impact of reduced societal costs for passengers of enhanced reliability in cost-benefit analyses (Van Oort et al. 2015a). These costs consist of passenger appreciation of a unit of time (variation) and the quantity of this time (variation). Most research (e.g. Li et al. 2010) focuses on the first part, while the latter, for instance reduction in (standard deviation of) travel time in a network context, still lacks insights (OECD/International transport forum 2010; Van Oort et al. 2015a). Contrary to car traffic (Snelder and Tavasszy 2010), calculating the quantity aspects for public transport is complex, since a schedule, waiting and transferring are involved. New data sources, as described in this paper, are helpful to calculate these aspects.
Causes and impacts of service unreliability
In order to improve public transport in general and its service reliability in particular, the main causes of disturbances must be understood. The next sections identify and analyse the main reasons for unwanted variation in public transport services and divides them into three components: namely driving time, stopping time and dwell time. It also distinguishes between the causes that are external and those that are internal, in order to clarify which actor is able to improve which element. This is illustrated by Fig. 2. These causes and relations are found by literature study (references will be provided in the next sections), by research and experiences of ourselves (Van Oort 2011) and by interviewing several authorities and operators via an international survey (Van Oort 2011, 2014). The fundament of our own research was data analysis (AVL and APC) of bus and tram lines in The Hague. In addition, field observations and interviews with drivers, planners and designers were performed enabling to derive the relationships.
Shalaby et al. (2001) found that trip time variation not only depends on service trip time (or line length) itself, but is also affected by the number of stops made, the number of signalized intersections passed, the vehicle intensity and the capacity per lane. Abkowitz and Engelstein (1983) found that line length, passenger activity and the number of signalized intersections affect average trip time. Most researchers agree on these basic factors (Abkowitz and Engelstein 1983; Levinson 1983; Abkowitz and Tozzi 1987; Strathman et al. 2000).
Considering the trip time components individually, we observed and analysed the different processes during a vehicle trip (of bus and tram in The Hague) to gain insights into the causes of trip time variability (Van Oort 2011), in addition to performing an international survey (Van Oort 2011, 2014) and studying literature. The first process we analysed was the driving between stops. This includes accelerating, braking and unplanned stopping. Since the causes of stopping may also be responsible for slowing down vehicles, both components are presented in one overview. The causes presented below are responsible for actual driving time and stopping time variability.
Driving and stopping time
Concerning driving and stopping times, we found the following main internal causes:
Driver behaviour. The basic driving style of every driver differs. Some tend to be more passive, whereas others are more aggressive. This results in variances in speed, stopping and braking of vehicles traveling along the transport network and thus in faster or slower journeys for passengers.
Other public transport. Both on the same route as on intersections, other public transport routes may affect the driving and stopping time variability. The influence is usually at its largest at signalized sections. Especially when frequencies are close to the theoretical capacity of a track, lane or intersection, this increases the probability of delays and increased service variability (Goverde et al. 2001; Van Oort and van Nes 2010; Landex and Kaas 2009).
Infrastructure configuration. The infrastructure (stops, lanes, intersections, terminals) may be designed in such a way that service variability could occur, for instance when the capacity of infrastructure is not sufficient to accommodate all traffic, or as a result of interaction with public transport at intersections. This could result in delays for (some) vehicles and therefore in increasing variability (Kanacilo and van Oort 2008; Van Oort and van Nes 2010).
Service network configuration. The configuration of the service network may influence the service variability. Examples are the number of lines on the same route, their length and the number of stops (Van Oort and van Nes 2009). The type of service network configuration may enforce other causes, for instance the impact of other traffic and driver behaviour. Longer lines, for instance, affect all causes mentioned. In the schedule, multiple lines may be presented as a higher frequent, coordinated service, while in practice service variability increases due to interaction between the different routes. Another example is the synchronisation of lines and schedules, which may be introduced to ensure transfers. This dependency of lines may lead to additional variability, since delays are transferred between lines.
Schedule quality. The schedule may affect the way drivers operate. If the quality of the schedule for instance is insufficient and margins are too small, some drivers will speed up to stick to the timetable, whereas others will operate like they are used to. This behaviour can introduce service variability (Van Oort et al. 2012).
The main external causes with regard to driving and stopping times are explained below:
Other traffic. The interaction with other traffic causes service variability as nearly every vehicle, at least to some extent, faces different operational conditions. This mainly occurs at intersections (regardless if they are controlled with traffic lights or have to cope without) or where public transport services share tracks or space with other transport modes or pedestrians. The extent to which this cause affects driving and stopping time variability depends on the level of dedicated public transport infrastructure. This may vary from at grade to exclusive lanes (with shared intersections) or mixed operations.
Weather conditions. Different kinds of weather and the resulting changes in driver behaviour may result in variability (Hofmann and Mahony 2005). This mainly occurs when the weather is not in the regular state since this disturbs regular processes.
The following internal causes for variability in dwell times may be distinguished (if not explicitly mentioned above):
Driver behaviour. The driver behaviour concerning opening and closing doors and the extent to which the vehicle waits for late arriving passengers is of influence on service variability.
Vehicle design. Weidmann (1995) and Fernandez (2010) showed the impact of the number and positioning of doors in vehicles, to obtain an optimized dwell process. Vehicles and/or platforms enabling same level boarding and alighting are also of influence and the same goes for the method of ticket handling. A suboptimal design, related to passenger behaviour, may result in dwell time variability.
Platform design. The platform design is also important because it affects passenger behaviour. The design may lead to a better distribution of passengers along the platform, enabling an optimal dwell process. Width, length, location of sheds and other facilities are important elements and if design is suboptimal, variability may arise.
The main external causes we found, are:
Passenger behaviour. Different types of passengers have different boarding speeds (for instance due to age, experience, luggage). The way passengers make optimal use of a vehicle’s doors is important, too. This is related to vehicle and platform design.
(Irregular) loads. Due to a different number of people boarding and alighting for every single trip, variability of dwell times will occur. Also crowding is an important cause of unreliable dwell times, being a result of large loads, passenger behaviour and vehicle and platform design.
On an aggregated line level, the number of stops is of importance, too. More stops usually cause a higher level of service unreliability. Thus line length is important, as is stop spacing.
The causes mentioned above are of importance when improving the level of service reliability as they identify the areas where enhancements may be applied. Quantifying the impacts of these causes is the next step. The following section deals with available data and improvement measures.
Improving service reliability
Lots of research has been carried out to identify the potential of improving travel time and service reliability. One could, for instance, think of improvements of vehicles, infrastructure, planning and operations. Literature shows that in urban public transport, substantial attention is given to ways to improve services at an operational level (Vuchic 2005; Ceder 2007; Cats 2014). Concerning strategic and tactical instruments, much research is already available. A lot of this focuses on the implementation of bus lane schemes, synchronisation and traffic signal priority, which are the most common solutions, as shown by e.g. Waterson et al. (2003). Ceder (2007), Vuchic (2005), and Lee et al. (2014) all present the different methods and effects and also give an overview of the issues which need to be considered in synchronisation. Potential instruments for improving operational quality during the design of the schedule are optimising trip time determination and holding (Delgado et al. 2012; Xuan et al. 2011).
Less researched so far, but also important, are the potential instruments that are available during network design, for instance, line length and design of terminals (Van Oort and van Nes 2009, 2010). Our study of a new tram line in Utrecht, the Netherlands (Van Oort 2012), showed that about 65 % of the (societal) benefits are related to service reliability aspects, coming down to a sum of over € 200 million during the total lifetime of the tram infrastructure. On busy bus trunks in Utrecht, a reduction of 30 s of trip time per bus saves about € 100,000 to € 400,000 in operational costs per year (based on costs per hour of operation of € 100).
The first step to increase operational performance is a proper analysis of historical operations. This paper focuses on bus and tram operations. The performance for heavy railways based on track occupation data is described in (Goverde and Meng 2011). AVL systems (Strathman et al. 2000, Hickman 2004) are of great help as these provide databases of historical performance with regard to travel time and reliability. Although such data have already been available for years, it is only recently that these can be accessed and used by Dutch transit authorities, researchers and developers. In addition to facilitating analysis of performance, these data also enable forecasts of future service quality (Kanacilo and van Oort 2008; Wilson et al. 2009). In this paper we present practical examples of data to illustrate the usefulness of these kinds of analyses: several bottlenecks are identified, providing transport authorities with insight into determining investment priorities. Next step is to add data from smart cards (APC; Pelletier et al. 2011) to gain insights into passenger impacts.