Challenge F: Even More Trains Even More on Time 1 Overcoming The

Challenge F: Even more trains even more on time

Overcoming the Constraints caused by Nodes on the Rail Network.

John Preston, John Armstrong and Melody Khadem Sameni. Transportation Research Group, School of Civil Engineering and the Environment. Chris Potts , School of Mathematics. Tolga Bektas and Banafsheh Khosravi, School of Management. University of Southampton, Southampton, UK.

Abstract

This paper will identify and assess innovative approaches to overcoming nodal capacity constraints on the rail network by examining the scope for technological improvements and operational changes. This will include examination of incremental changes, such as improved design of points, changes in signal spacing and overlaps, but also more radical changes including concepts from other modes (e.g. intelligent speed adaptation) and a relaxation of the Rules of the Route/Plan. A layered approach is adopted by examining nodes of increasing complexity on Great Britain’s national rail network. This paper will focus on work undertaken on the South West Main Line (London Waterloo – Southampton Central) but will also consider the application to more complex nodes on the East Coast Main Line between Huntingdon and Grantham. Our methodology will consist of two main elements. First, we will provide a state of the art review which will examine how nodal capacity problems have been tackled to date in Britain and overseas. We will also briefly examine systematic approaches to innovative problem solving, as proposed by the TRIZ methodology, general systems theory and the theory of constraints. Second, we will develop a generic meso-level model and simulation tool, based on RailSys, which will determine train routeings and schedules, levels of disruption and reactionary delay and measures of capacity utilisation at nodes. We will also outline two alternative approaches. The first set of these focuses on micro-level optimisation by applying production scheduling techniques to rail scheduling, such as shifting bottleneck procedures and local search approaches. The second set of approaches involves integrating simulation and optimisation models by using multi-commodity integer programming formulations. We will present some preliminary findings on the scope for technological solutions (such as enhancements to signalling, switches and crossings) and operational solutions (such as dynamic traffic management) to enhance nodal capacity and overcome bottlenecks.

1. Introduction

This paper is based on the OCCASION (Optimising Capacity Constraints: A Simulation Integrated with Optimisation of Nodes) research project, funded by the UK’s Engineering and Physical Sciences Research Council (EPSRC) and Rail Safety and Standards Board (RSSB), that commenced in October 2010 and is due to be completed in September 2012. OCCASION aims to identify innovative approaches to reducing and overcoming nodal (i.e. junction and station) capacity constraints on railway networks, making use of both technological and operational improvements, and to combine simulation and optimisation tools to produce an integrated assessment of possible means of reducing the effects of these constraints. More details of the OCCASION project are provided in Armstrong et al., 2011a.

The theory of constraints indicates that the capacity of a system is dictated by the potential capacity of its key constraints or bottlenecks (McMullen, 1998). For the railway industry, the key constraints are imposed by the nodes (junctions and stations) (see, for example, Pachl, 2009, and Hansen and Pachl, 2008). At junctions, capacity is reduced by the traditional design of points (and the time required for them to be set to the correct position) and by rules permitting trains to move (which assume a speed profile that will allow trains to stop before reaching a junction). In turn these rules are determined by factors such as sighting distance, braking distance (including a safety margin) and overlaps. At stations, capacity is lost by the routeings into platforms (essentially an extension of the issues related to junctions), by signal spacings and technology at stations and by dwell times, which in turn are related to passenger embarkation and

Challenge F: Even more trains even more on time

disembarkation times. This project will examine the extent to which technological changes (e.g. in-cab signalling, closed loop control, improved point design) and operational changes (including changes to the operational planning rules) can limit these capacity losses.

The overall objective of the project is thus to identify means of increasing the capacity of nodes (i.e. junctions and stations) on the network without making major investments in new infrastructure (i.e. grade separation, provision of avoiding lines, etc.). For example, work by Cho (2009) has shown that dynamic rescheduling tools can obviate the need for additional infrastructure at key junctions on Britain’s East Coast Main Line (ECML) such as Cambridge Junction, north of Hitchin. OCCASION will examine whether this finding also holds for stations, such as Peterborough (also on the ECML), which may be viewed as complex nodes.

This objective can be approached from various directions and by a range of techniques, but the focus of OCCASION is on the use of simulation and optimisation techniques to identify the potential benefits of improved train scheduling and routeing through nodes, and to investigate the potential benefits of technological improvements (such as improved switches and crossings) and relaxations and/or amendments of train planning and timetabling guidelines (the ‘Rules of the Plan’ in the British operating context).

Given the above, the structure of the paper follows the broad structure of the OCCASION project (see Figure 1) and is as follows. In section 2, we provide a succinct literature review. In section 3, we provide some preliminary results on the application of simulation models to the nodal capacity issue, with respect to a case study of the South West Main Line and a future case study ofthe ECML. In section 4, we examine some alternative approaches including micro-level optimisation methods and tactical planning models. In section 5, we draw some preliminary conclusions.

Capacity enhancement measures

Meso-level simulation tool Developing a Tactical Literature conceptual model Case review for analysing planning studies model capacity at nodes Micro-level optimisation tool

Figure 1: Conceptual outline of the OCCASION project

2. Literature Review

Relevant recent work in the UK includes that undertaken for the 2007 Rail Technical Strategy (and the 2010 update) (TSAG, 2010) and for RSSB (Barter, 2010), along with work we have already undertaken (Khadem-Sameni et al., 2010a). Network Rail’s Route Utilisation Strategies, or RUSs, are also relevant, notably the recently-published draft RUS for consultation for London and South East England, which includes detailed coverage of the South Hampshire and Solent area) (Network Rail, 2010). Our initial review indicates that practice in Britain lags behind that in continental Europe, with a focus on increasing on-train capacity through the use of higher seating or standing densities, longer trains and on the automation of routine tasks, such as the use of Automatic Route Setting (ARS) to assist signallers and controllers. Indeed, it seems that recent and current focus on train punctuality has led to increased journey times through the provision of recovery time and allowances, and thus in increased consumption of line, route and network

Challenge F: Even more trains even more on time

capacity, with rail users experiencing improved punctuality, but at the expense of increased journey times.

We are therefore keen to make use of recent theory and best practice developed outside the UK: for example, the Dutch Triple A program advocates a different approach to operations (in particular by removing conflicting movements), to capacity allocation (by examining marginal claims) and to capacity enhancements (through the use of dynamic traffic management) (Kraaijeveld, 2009a). With respect to dynamic traffic management, we are interested in the use of zones of concentration and compensation in Switzerland (Lüthi, 2008) and the related concept of slot slipping, developed in the scheduling of personal rapid transit systems (Lees Miller, 2011). We are also investigating innovative problem solving techniques to the capacity challenge such as applications of general systems theory (Skytnner, 2001) and use of the 40 principles of TRIZ (Altshuller, 1999). For example, removing conflicting movements might be related to TRIZ principle 2 (taking out), marginal claims may be related to principle 10 (preliminary action) and dynamic traffic management might be related to TRIZ principle 23 (feedback).

Following Barber et al. (2007), our initial review of software tools for capacity assessment and management has identified several candidate models. These include the following: (i) RailSys; (ii) OpenTrack; (iii) VISION; (iv)DONS (and its CADANS, STATIONS and SIMONE modules); (v) PETER; (vi) ROMAN; (vii) VIRIATO (and its CAPRES capacity module); (viii) DEMIURGE; (ix) RAILCAP; (x) CMS (Capacity Management System); and (xi)RTC (Rail Traffic Controller). Of these, the first three are microscopic simulation tools which can be used to assess the performance of different infrastructure and timetable combinations, running under planned and perturbed conditions. While they can identify conflicts, and perform some re-routeing under perturbation, this is only done in accordance with user-input criteria, and they do not automatically generate improved timetable solutions. We propose to use RailSys to obtain running time data and to calibrate and validate our mesoscopic modelling activities.

According to Barber et al., SIMONE performs a similar role to the models described in the preceding paragraph, but in the specific context of DONS, Railned’s system for the Design of Network Schedules, whereas CADANS and STATIONS produce initial timetables and detailed station routeings, respectively. STATIONS is therefore of particular interest in the context of the OCCASION project. The Dutch model PETER is used to assess timetable performance and robustness in the context of disruptions, while ROMAN is used in Germany and Austria to generate and simulate timetables, but does not have a timetable optimisation facility. The Swiss VIRIATO software provides a comprehensive range of functionality, including the planning of multiple, regular interval trains, and individual services. It provides track and station occupation data, rolling stock rostering functionality, running time calculations, and trip time analysis. VIRIATO’s CAPRES module, which generates saturated timetables, taking account of junction and station characteristics and constraints, is again particularly relevant in the current context. SNCF Engineering’s DEMIURGE model has similar capabilities to those of CAPRES, and is thus also of particular interest, while the Belgian RAILCAP model conducts assessments of capacity utilisation/consumption but, according to Barber et al., it is quite time-consuming to use, although a version has been produced that is easier to use, and provides similar quality of output. CMS is a combination of computer models used in the UK, of which the Planning Timetable Generator (PTG) tool is of most relevance to the current project; further information on its functionality and implementation is being sought from Network Rail, the model’s owner. We understand that RTC can be used to resolve conflicts and suggest schedule changes, but its North American origins are likely to mean that it is more relevant to long-distance freight traffic modelling than to the detailed assessment of junctions and stations carrying predominantly passenger services (Lai and Barkan, 2009).

3. Simulation Modelling

Although capacity issues have been studied at the macro (‘broad brush’, network) level (for example, in Britain, using the Department for Transport’s Network Modelling Framework) and at

Challenge F: Even more trains even more on time

the micro (detailed studies of nodes or links) level, using software tools such as RailSys, OpenTrack and Vision (discussed above), we believe that, despite the preparation by Network Rail of Route Utilisation Strategies, there is something of a gap at the intermediate, meso-level. This is analogous to the mesoscopic level of infrastructure modelling referred to by Radtke (2008) and reflects the observation by Hansen and Pachl (2008, p210) that

existing analytical approaches need to be further developed … for the estimation of capacity in bottlenecks, especially in complicated yards and at major stations.

We therefore plan to develop a modelling tool that will be used to identify all possible routes through the junctions and stations under review, including platform arrivals and departures and non-stopping through movements, identifying individual nodes and links used (i.e. the “single channel systems” referred to by Pachl, 2009), the interactions between different routes (i.e. conflicts), route occupation times, and train sequences through the junction or station, and to calculate node and link occupancy levels (and thus identify bottlenecks within the junction and station interlockings). Parallel (i.e. non-conflicting) routes allowing simultaneous train movements will be identified, and the minimum intervals, or headways, between platform occupations and successive or conflicting movements will be established. Route and segment running times will be obtained from RailSys (version 6), and minimum headways will be obtained from the Rules of the Plan. The tool’s initial development is described by Armstrong et al., 2011b.

Once developed, the tool will be used to develop and assess alternative routeings and schedules of trains through a junction or station. The associated percentage occupancy levels for individual nodes and links can then be evaluated, providing the basis for individual and overall measures of capacity utilisation/consumption. RailSys and/or related modelling and performance data analysis techniques will be used to assess the levels of disruption and reactionary delay associated with different levels of capacity utilisation, with a view to establishing empirical maximum recommended values, equivalent to those already available for sections of plain line. This will include consideration of both the Union Internationale de Chemins de Fer (UIC – International Union of Railways) timetable compression approach (UIC 406), and the more simplified UK approach based on the Capacity Utilisation Index (CUI) (UIC, 2004; Gibson et al., 2002) We will build upon the approach used by Arup (one of the industrial partners in OCCASION) for the Waterloo International RailSys project, which sought to identify the best method of providing additional capacity in terms both of additional (Waterloo International) and longer platforms. We propose to undertake a layered approach to tackling this problem. We would begin by assessing relatively simple nodes on the South West Main Line (SWML) – detailed briefly in section 3.1, before moving on to the more complex ECML case study, sketched briefly in section 3.2.

3.1 South West Main Line Case Study

The South West Main Line (SWML) runs from London Waterloo to Southampton and onwards to Bournemouth and Weymouth. There are numerous branches off the main line, most notably to Guildford and Portsmouth (at Woking Junction), to Alton (at Pirbright Junction) and to Salisbury and the West (at Worting Junction). We have assessed capacity utilisation using the UIC406 and CUI timetable compression methods, suitably modified, for a central section of the route (Southampton Central to Basingstoke) for the morning peak (0700 to 1000) for trains in the Up direction (towards London) (see also Khadem Sameni et al., 2011). The results are illustrated by Figures 2 and 3 and Table 1.

Eastleigh St. Denys

Southampton Southampton Swaythling Shawford Winchester Waller’s Micheldever Worting Basingstoke Central Airport Parkway Ash Junction

35.5% 30.5% 32.2% 31.6% 35.5% 42.7% 31.6% 31.6% 33.8% 27.2% Figure 2 - Timetable compression according to CUI method.

Challenge F: Even more trains even more on time

Eastleigh St. Denys

Southampton Southampton Swaythling Shawford Winchester Waller’s Micheldever Worting Basingstoke Central Airport Parkway Ash Junction

30.1% 39% 28.6% 30.5% 40% 21.8% Figure 3 - Timetable compression according to the UIC 406 method.

Comparing the results it is seen that the average capacity utilization index by the CUI method (based on route sections) is 1.7% higher than the UIC 406 method (based on signal blocks or aggregations thereof) for the route between Southampton Central and Basingstoke. This can be explained by the fact that in the CUI method, headway is considered at the start or end of a link, while in the UIC 406 method blocking time is considered for any part of the link (see also Table 2). However, to generalize the results, more case studies are needed, particularly as the results are sensitive to aggregation. For example, if the UIC calculation is based on 10 blocks rather than six (and hence made consistent with the CUI calculation), the mean capacity utilisation becomes 26.7%, with a maximum of 31.6% (see also Table 3). The CUI method is based on headways so that when there is a change from quadruple tracks to double tracks at Shawford station, the slow headway increases from two and a half minutes to three and a half minutes. Therefore, the capacity utilisation for the Shawford to Winchester link is considerably higher than the UIC 406 result and this might be seen as partially taking into account nodal capacity constraints. As a result the CUI method gives particularly high levels of utilisation for the route between Eastleigh and Basingstoke but gives lower levels of utilisation than the UIC method for the route between Southampton Central and Eastleigh.

Table 1 Comparing UIC 406 and CUI results for the SWML case study.

Southampton Eastleigh- Southampton Central – Eastleigh Basingstoke Central- Basingstoke

UIC 406 34.5% 30.2% 31.7%

Mean CUI 32.5% 33.7% 33.0%

Difference +1.7% ( CUI - UIC 406) -2.0% +3.5%

UIC 406 39% 40% 40% Max CUI 35.5% 42.7% 42.7%

Challenge F: Even more trains even more on time

Table 2 An overview of the UIC 406 and the CUI methods

UIC 406 CUI Considers blocking time at links Considers either “slow” or “fast” headways for route sections More detailed Less detailed Applied in the continental Europe Applied in Great Britain According to the general UIC 406 standard and According to the Network Rail’s Rules national railways’ specifications of Plan

Table 3 (based on UIC 406 calculations using RailSys version 6) and Figure 4 illustrate how capacity utilisation adds up as more trains merge towards London Waterloo. There is a sudden jump in capacity utilisation from Shawford to Winchester due to the change from quadruple tracks to double tracks. During the morning peak period, there are no freight trains, and due to platform restrictions at London Waterloo station, no more passenger trains can be added during the morning peak hours. An interesting finding is that average capacity utilisation per train (capacity utilisation divided by number of trains) is around 1.6%, although it increases as the length of line section increases. It starts from 1.04% and reaches 1.96% at Worting Junction.

Table 3 - Capacity utilisation from Southampton Central station towards London Waterloo during morning peak hours

Capacity Capacity Number of Average utilisation utilisation from trains (7:00 capacity from Southampton am- 10:00 utilisation previous Central am) per train node to this (Cumulative) node (Individual) St. Denys 30.1% 30.1% 29 1.04% Swaythling 21.2% 33.5% 31 1.08% Southampton 20.7% 36.3% 31 1.17% Airport Parkway Eastleigh 24.7% 43.2% 31 1.39% Shawford 28.6% 55.6% 39 1.43% Winchester 30.5% 70.5% 40 1.76% Waller’s Ash 30.9% 77.8% 40 1.95% Micheldever 27.8% 81.1% 41 1.98% Worting 31.6% 84.3% 43 1.96% AVERAGE 27.3 56.9% 36.1 1.58%

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90.00% 81.10% 80.00% Cumulative Individual 77.80%84.30% 70.00% 70.50% 60.00% 55.60% 50.00% 33.50% 43.20% 40.00% 28.60% 30.90% 31.60% 30.00% 30.10% 36.30% 27% 20.00% 24.70% 30.50% 21.20%20.70% 10.00% 0.00%

Worting St. Denys EastleighShawford Swaythling Winchester Waller's AshMicheldever

Southampton Airport Parkway Figure 4 Capacity Utilisation for the Morning Peak Hours (0700-1000)

This provides an outline illustration of the impact of nodes on capacity utilisation. The average link capacity utilisation is only 27.3% but the link and node capacity utilisation is 56.9%. This suggests that, for this stretch of line, nodes (stations and junctions) account for over half of the capacity utilisation on average. At a particular bottleneck, such as Worting Junction, the node accounts for almost two-thirds of capacity utilisation. Armstrong et al. (2011b) find similar results – using a CUI methodology, the capacity utilisation at Pirbright Junction (up the line from Worting) is found to be 35.3%, whilst the capacity utilisation at Southampton Airport Parkway is found to be 37.7%. These nodal capacity utilisations are both in excess of the link utilisations indicated in Table 3, although it should be noted that they are based on the peak hour only (0800 to 0900).

3.2 East Cost Main Line Case Study

The nodes we have examined in the SWML case study are relatively simple. We propose to extend our approach to the East Coast Main Line (See Figure 5 for a schematic representation). It can be seen that this line exhibits variation in the number of tracks provided and has two modestly complex stations at Peterborough and Grantham. It carries a mixture of fast long distance services and semi-fast and slow commuter services, along with some freight traffic. There are also conflicting regional services crossing the line at both Peterborough and Grantham. As a result this section has been identified as a major bottleneck (see also Network Rail, 2008).

Huntingdon platforms) (2 Huntingdon Jn. North Connington North Jn. Fletton Peterborough Platforms) (5 Jn. Helpston Stoke Grantham Platforms) (4

4 tracks 3 tracks 2 tracks 3/5 tracks 4 tracks 2 tracks 3 tracks

Figure 5: A Schematic Plan of the East Coast Main Line between Huntingdon and Grantham.

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4. Optimisation and Integration

4.1 Micro-level optimisation

A complementary approach to the meso-level simulation discussed above is micro-level optimisation. Examples are given by the work of De Luca Cardillo and Mione (1999) and Billionnet (2003) who have considered a conflict graph model, based on a node packing formulation, for a simplified version of the train platforming problem, where the train arrival and departure times are fixed and the choice of platform defines a unique route for trains. More general models have been considered by Zwaneveld et al. (1996), examining the feasibility of allocating platforms to a combination of trains for the purposes of strategic planning and follow-up work by Zwaneveld et al. (2001) has considered the use of a node packing model for the routeing of trains through stations. Kroon et al. (1997) have considered more general models where the arrival and departure times and routes are not determined a priori, investigating the complexity of this problem, which is proved to be NP-complete when trains have more than two options for routeing. This work provides part of the basis of the ‘STATIONS’ decision support system in the Netherlands, mentioned above, that routes trains through stations based on available capacity and on safety and service requirements.

The manual process of scheduling and routing of trains through complex stations was mathematically modelled in the UK by Carey and Carville, 2003. They introduced binary variables to consider platform feasibility (connection, proper length, special needs, etc.) for each train, using the concept of platform desirability. Some costs are considered to differentiate between more desirable platforms as well as platform obstruction costs when a platform accommodates more than one train and one of the trains obstructs the path of the other. If trains cannot be assigned to the most desired arrival and departure times (due to occupation of platforms and conflicts), time adjustment costs are considered. Trains are scheduled and routed in chronological order, and path/platform conflicts and minimum headways are checked and resolved. Carey and Crawford (2007) extended this work to a network comprising corridors as well as stations.

With a similar node packing formulation to Zwaneveld et al. (1996), the robust routeing problem has been solved for Bern station in Switzerland by Caimi et al. (2007). They propose a fixed-point iteration and a local search heuristic to find delay-tolerant train routes. Caprara et al. (2007) suggest an integer linear programming formulation based on the conflict graph model for the train platforming problem, which is basically the train routeing problem through a railway station. They consider a quadratic objective function associated with the costs of platform assignments to trains. The objective function is then linearised, so that the linear programming relaxation can be exploited to develop a branch-and-cut-and-price heuristic.

For the specific purposes of the OCCASION project, priorities need to be decided at junctions, and trains need to be assigned to platforms at stations. Our approach is to exploit the relationships between these problems within the rail network, and problems of scheduling jobs on machines in production industries. Since production scheduling has been widely studied for the past fifty years, there is a huge body of literature that provides exact and approximate solution approaches for different environments (for example deterministic, stochastic, static and dynamic) that can be exploited - see, for example, Pinedo (2008), Brucker (2007) and Blazewicz et al. (2001).

To transform railway scheduling problems using production scheduling models, we regard trains as jobs and nodes or links as machines. The relevant production scheduling model is typically a general version of a job shop, since different routeings of trains through the rail network correspond to jobs passing though machines in different orders. Further, the different possible choices of platform for a train can be modelled by a job having a choice of machines it can visit. Although the relationship between train scheduling and production scheduling has been known

Challenge F: Even more trains even more on time

for some time, since the pioneering work by Szpigel (1973), its exploitation for solving practical problems is recent. Oliveira and Smith (1990) and Rodriguez (2007) offer a constraint programming approach to this problem, whereas D’Ariano et al. (2007), Corman et al. (2009) and Liu and Kozan (2009) present an alternative graph formulation of the job shop scheduling problem. Thus, our goal is to extend the approach of Liu and Kozan, which was based on Australian railways, to include relevant features of the UK rail network.

There are a variety of solution approaches for job shop scheduling that can translate into algorithms for train scheduling. At a basic level, the dispatching rules that have been widely- studied for production scheduling (Haupt, 1989) are suitable for real-time scheduling of the rail network. However, with the increased computer power currently available, more sophisticated approaches can potentially produce better schedules. The shifting bottleneck procedure developed by Adams et al. (1988) is one approach to be investigated. Other approaches to be used include local search, such as tabu search and variable neighbourhood search (for a review, see Anderson et al., 2003). Local search has been successful for the job shop, albeit at significant computational expense, and faster versions of the more successful approaches will be developed for scheduling trains through nodes.

A recent study by some of the authors of this paper (Khosravi et al., 2011) treats the scheduling of trains as a job shop scheduling problem with additional constraints for safety and operational issues. The aim is to consider more realistic and detailed constraints in the UK railway industry. This study addresses the train scheduling problem at the micro level including detailed information about the tracks and train movements. The model also differs from the conventional job shop scheduling problem in terms of its objective function. Whereas the well-known job shop scheduling minimizes the total completion time, the model discussed here minimizes total weighted lateness in order to minimize the delay propagation of different types of trains in the railway network. The algorithm creates a conflict-free schedule for trains defining entry times and order of the trains on track sections with predetermined routes.

As train scheduling is a very large and complex combinatorial problem, heuristic methods are more appropriate to use. A new solution method inspired by the successful results of the shifting bottleneck procedure for job shop scheduling problem has been developed. In this study, the shifting bottleneck procedure is adapted to solve the problem of minimizing total weighted tardiness. The proposed method finds the bottleneck machine using an enumeration algorithm and it consists of an additional re-optimization step to re-sequence jobs on the previously scheduled machines. Performance results for the suggested algorithm are compared with the FCFS (First Come First Served) dispatching rule and a local search heuristic. Data sets are based on a bottleneck area in the South East of the UK. It is a dense and complicated network in terms of junctions and stations structure with both passenger and freight traffic.

4.2 Tactical Planning Model

The meso-level simulation tool will be integrated with the micro-level optimisation algorithms through the development of models for service network design at a tactical level, where capacity versus service quality trade-offs associated with various service levels, and subject to safety considerations, are easier to represent and evaluate (Crainic and Rousseau, 1986; Bektas et al., 2010). It is anticipated that the framework will encompass a mathematical model in the form of a multi-commodity (fast and slow passenger trains, freight trains) integer programming formulation for a scheduled service network design problem with fleet management (Andersen et al., 2009), and will incorporate dynamic adjustment methodologies for improving node (station, junction) performance using innovative ideas from rail freight transportation (Bektas et al, 2009).

5. Conclusions

The importance of the impact of nodes on rail capacity utilisation is relatively well known. This paper has illustrated that, for a relatively well-used line, over half of the capacity utilisation might

Challenge F: Even more trains even more on time

be attributed to nodes, and this fraction may be even higher at bottlenecks on the network. It is therefore understandable that where there are capacity constraints, the initial focus should be on nodes. In particular, the reduction of station dwell times, junction margins and minimum headways could have a big impact on capacity, but this should not be at the expense of reliability or safety. Measures that will be investigated by the OCCASION project will include the innovative design of switches and crossings (in conjunction with one of our industrial partners, Balfour Beatty Rail) and the role of dynamic traffic management, building on the related concepts of Next Generation Traffic Management and Driver Advisory Systems (TSAG, 2010).

References

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