Cross-Reference to Related Applications

[0001] This application claims benefit to and priority of UK Patent Application No. GB2007104.9 “A method and infrastructure for boarding a plurality of passengers into an autonomous vehicle”, filed on 14 May 2020.

Field of the Invention

[0002] The invention relates to a computer-implemented method for boarding a plurality of departing passengers into an autonomous vehicle at a stop as well as the associated infrastructure.

Background to the Invention

[0003] The term “automated transit network” or “automated transportation network” (abbreviated to ATN) is a relatively new designation for a specific transit mode that falls under the larger umbrella term of “automated guideway transits” (AGT). Before 2010, the name “personal rapid transit (PRT)” was used to refer to the ATN concept. In Europe, the ATN has been referred to in the past as “podcars”. This document sets out a method for improving the boarding process into an autonomous vehicle using the ATN.

[0004] Like all forms of AGT, ATN is composed of autonomous vehicles that run on an infrastructure and are capable of carrying passengers from an origin to a destination. The autonomous vehicles are able to travel from an origin stop at the origin of the passenger’s journey to a destination stop at the destination without any intermediate stops or transfers, such as are known on conventional transportation systems like buses, trams (streetcars) or trains. The ATN service is typically non-scheduled, like a taxi, and travelers are able to choose whether to travel alone in the vehicle or share the vehicle with companions.

[0005] The ATN concept is different from self-driving cars which are starting to be seen on city streets. The ATN concept has most often been conceived as a public transit mode similar to a train or bus rather than as an individually used consumer product such, as a car. Current design concepts of the ATN currently rely primarily on a central control management for controlling individually the operation of the autonomous vehicles on the ATN. By comparison, self-driving cars are autonomous and rely on self-contained sensors to navigate. [0006] A report on “Automated Transit Networks (ATN): A Review of the State of the Industry and Prospects for the Future” published by the Mineta Transportation Institute, Report No 12-31 in September 2014 reported that at the date of writing no ATN having more than ten stations had been implemented in the world. Currently the ATN networks operate on the principle of mapping each origin to all of the destinations. This leads to a matrix with 20 entries even for a simple five-station system as there are four possible destinations from each of the five origins. A ten-station system would have 90 possible routes and it will be seen that as the number of origins and destinations increases, then an O/D matrix listing all of the possible routes will expand out of hand. The current systems are therefore not scalable.

[0007] The passenger capacity of transit networks is one of the technical parameters used to characterize transport modes and also of automated transport networks, as the passenger capacity is indicative of the technical performance of the transport mode. The passenger capacity is defined as the number of passengers that can be serviced per time-unit and is affected by different technical aspects of the transport mode.

[0008] One of the bottlenecks in an efficient operation of the ATN is the decrease of the passenger capacity due to an inefficient boarding process into the autonomous vehicle at the origin stop at which the passenger will board the autonomous vehicle. It is known on conventional systems that overcrowding of certain areas of a stop reduces the passenger capacity. This overcrowding of certain areas of a stop can lead to alighting passengers impeding waiting passenger waiting to board the vehicle and decreases the rate of passengers boarding and/or deboarding arriving vehicles at this stop. The boarding / deboarding of one vehicle takes longer than at least theoretically necessary. The increase in the time for boarding / deboarding leads to an increase in travel time for each passenger and correspondingly leads to a decrease of the passenger capacity of the transport network. It is also the case that passenger flows may not be distributed equally among all of the autonomous vehicles at the autonomous stops.

[0009] The overcrowding of certain areas of a stop can also cause perturbations of the flow of the autonomous vehicles in the vicinity of a stop due to the longer boarding / deboarding times. Those vehicles being boarded need to wait at the stop for a longer time than necessary and therefore block the stop for subsequent vehicles longer than necessary. This additional waiting time can cause congestions on the tracks of the automated transport network causing further delays and lead to a further decrease of the passenger-carrying capacity of the automated transport network. [0010] Another reason for congestions on the tracks of the automated transport network in the vicinity of a stop can be high passenger demand. If the demand from passengers wanting to depart from the respective stop within a certain period of time is bigger than the capacity of the stop for the respective period of time, too many automated vehicles would arrive to pick up passengers at the stop simultaneously. This can lead to a congestion. As described above, this leads to a decrease of the passenger capacity.

Prior Art

[0011] A number of prior art documents are known which attempt to address the problem. For example, US Patent Application No. US 2019/0228664 (Seki, assigned to Subaru Corp) teaches a vehicle calling system for enabling a user to call for a vehicle at a user terminal.

The user is presented with a list of boarding locations and the user can select the desired boarding location. The autonomous vehicle then proceeds to the selected boarding location. The method disclosed in the patent application could be designed to only select those boarding locations with the least number of passengers. However, the system described does not teach how to manage waiting passengers that are already at the stop. Furthermore, the system allocates an autonomous vehicle in advance to the user. The autonomous vehicle could block the stop whilst waiting for the user to arrive.

[0012] A similar concept is known from US Patent No. 6,394,231 (Schuster et al, assigned to Inventio/Schindler).

[0013] US Patent No 10,440,536 (Waymo) offers one solution to speeding the boarding of the passengers. A vehicle reaching a pickup location attempts to authenticate a client device, such as a smartphone, to determine the location of the passenger. The vehicle then stops to enable the passenger to enter the vehicle.

[0014] US Patent No 10,372,141 (Donnelly et al, assigned to Uber) teaches a method in which boarding times into the autonomous vehicles are determined for different passengers based on data associated with the user. This method will allow efficient allocation of the autonomous vehicles, but does not solve any conflicts of waiting passengers at a stop.

[0015] US Patent No. 10,276,048 (Beaurepaire, assigned to Here Global) teaches the management of a boarding area for an autonomous vehicle.

[0016] International Patent Application No. WO 2014/121329 Al (Kirchner, assigned to Univ. Sydney Tech.) describes a system, method, and components for influencing crowd behavior at a station. The system and method disclose monitoring of input data indicative of crowd movement pathways from input devices such as sensors or cameras and detecting of, for example, overcrowding in areas of the station. The system and method then perform corrective actions such as activating one or more crowd influencing devices. The method focusses on dealing with detecting and influencing the behavior of a crowd of multiple passengers and does not take the management of single passengers into consideration. In contrast, the present application focuses on managing the movement of single passengers. [0017] International Patent Application No. WO 2019/183662 A1 (Morris, assigned to Airboardx Pty. Ltd.) describes a boarding system and method comprising one or more repositories, a processor and one or more electronic devices. The one or more repositories are designed for storing items of data on a passenger, a booking, and a booked vehicle of a specific scheduled journey in the vehicle. The processor is adapted to calculate grouping of one or more passengers boarding the vehicle, an optimized boarding order, and boarding times for every group of passengers based on one or more of said items of. The passengers or groups of passengers are notified to go on board through the one or more electronic devices. The grouping is conducted based on passenger characteristics like, e.g., age or mobility issues.

[0018] US Patent Application No. US 2015/0369621 A1 (Abhyanker) describes a method and a system of variable “ad-hoc” bus stops across a bus route in a regional transportation network. The patent application further describes creating variable stops for specific passengers, depending on the location and number of the prospective passengers. The ad-hoc bus stops are only created if a threshold number of prospective bus passengers are and/or will be at the shared ad-hoc bus stop. The passengers are only directed to the ad-hoc bus stop if the calculated number of passengers in the vicinity is greater than a threshold value.

[0019] US Patent Application No. US 2013/0161450 A1 (Klettke, assigned to Airbus Operations GmbH) describes a method, computing unit and directing system for allocating boarding paths to a vehicle to the passengers of that vehicle. Before allocating boarding paths to the passengers, the seats available in the vehicle are divided into two or more changeable groups. The boarding paths are allocated to the different seat groups. The forming of the changeable groups is not done depending on the actual number of passengers that want to board the vehicle, and only depends on the available seats of the vehicle.

[0020] Chinese Patent Application No. CN 109840982 A (Li, assigned to Boe Technology Group Co. Ltd.) discloses a method for recommending one out of a plurality of queues e.g., in a supermarket with the goal of reducing the waiting time for the customers. The recommendation is described to be executed based on image information of the queues, which is a difference to the present application. The present application claims the use of passenger information specific to the departing single passenger instead of information about a plurality of passengers. The image information used in CN 109840982 A is information about a plurality of passengers instead of information specific to single departing passengers. CN 109840982 A does not describe how single passengers are directed to the determined area, which is a feature of the present application.

[0021] None of the cited patents address the need to manage the boarding areas at a stop to try to manage the boarding and de-boarding process from the autonomous vehicle.

[0022] There is furthermore a need to manage departure of approaching passengers after a mass event, such as an arrival of a train at a rail station, or at the end of a concert or football match in a stadium.

Summary of the Invention

[0023] A computer-implemented method for boarding a plurality of departing passengers into a plurality of autonomous vehicles at a stop is disclosed in this document. The method comprises receiving, by one or more detection system processors, passenger information from one of mobile devices or ticket machines of the plurality of departing passengers. The passenger information comprises at least details of the stop at which the departing passenger wishes to board and a passenger identification. The passenger information may also include the desired departure time, such as “immediate”, “in ten minutes” or “at 10 am”, as well as any additional passenger requirement, such as but not limited to mobility restrictions.

[0024] In a next step, a number of the departing passengers proximate to the stop is calculated, at the one or more detection system processors, from the passenger information and, if the number of the departing passengers is calculated by the one or more detection system processors to exceed a threshold value on a time forecast basis, then the method sends direction messages from the one or more detection system processors to one of the mobile devices of the plurality of departing passengers or the ticket machines directing to an alternative one of a plurality of areas in the stop, rather than to the closest one of the plurality of areas at the stop.

[0025] This method enables the distribution of the departing passengers about the stop to minimize the risks of overcrowding at any position at the stop.

[0026] The number of departing passengers can be forward calculated for a period of time to see whether the threshold value will be reached in the next period of time and pro-active direction of the departing passengers can be initiated even if the threshold value has not yet been reached. The calculation of the number of departing passengers is made, for example, by considering approaching passengers in a detection zone proximate to the stop, who have not yet arrived at the stop.

[0027] In a further aspect, the method sends direction messages from the one or more detection system processors to one of the mobile devices of the plurality of approaching or departing passengers directing to different ones of the stops or an alternative one of a plurality of areas within the stop.

[0028] The boarding area is divided into a plurality of areas. These areas can be divided by one of signage, lighting indications or physical barriers.

[0029] The method also enables the number of departing passengers to be balanced with the number of available autonomous vehicles.

[0030] The direction messages may further include guidance directions to the plurality of areas through one or more “choke points”. The choke points restrict the flow of the departing passengers heading to the areas to reduce further a risk of overcrowding.

[0031] In a further aspect, the method can be used to direct the departing and/or approaching passengers at the end of a mass event, such as the arrival of a train at a rail station or the end of a concert or football match in a stadium. Control signals are received from a mass event controller by the one or more detection system processors and direction messages are sent from the one or more detection system processors to the plurality of departing and/or approaching passengers to direct them to one of the areas in the stops, even if the threshold value is not reached.

[0032] In a further aspect of the method, wherein the method takes into account one of the user devices entering a detection zone about the stop, even if a possessor of the user device has not yet booked a trip on the autonomous transportation network. In this case, the method can also take into account a usual route, a preset route or user default route from the user device to forecast demand in the next period of time.

[0033] In a further aspect, the method does not specify a boarding order for the departing passengers waiting in ones of the plurality of areas, but the departing passengers arrange in an order for boarding the ones of the plurality of autonomous vehicles. There is no kind of grouping of the departing passengers based on passenger characteristics of the passengers. The actual number of the departing passengers gained from the passenger-specific passenger information is the basis for directing the passengers to different areas in the stop.

[0034] A boarding infrastructure for enabling boarding of an autonomous vehicle by a plurality of departing passengers at a stop is also disclosed. The boarding infrastructure comprises a plurality of areas at the stopping point and a detection system for calculating the number of the plurality of departing passengers at the stop. A transmission device for receiving messages from the mobile devices held by the approaching passengers or departing passengers. The transmission device is also configured for transmitting direction messages to ones of the plurality of departing passengers indicating one of the plurality of areas for boarding is also disclosed.

[0035] It will be appreciated, that the boarding infrastructure comprises a detection system for forward calculating the number of the plurality of departing passengers at the stop or for forward calculating the number of the plurality of departing passengers at the stop from the approaching passengers or departing passengers in a detection zone proximate to the stop, who have not yet arrived at the stop.

[0036] In a further aspect, the boarding infrastructure comprises a transmission device for transmitting direction messages to ones of the plurality of approaching or departing passengers indicating different ones of the stops or the plurality of areas.

[0037] A computer-implemented method for boarding a plurality of approaching passengers into a plurality of autonomous vehicles at a stop proximate to a mass event is also disclosed. The method comprises receiving, by one or more detection system processors, passenger information from one of mobile devices or ticket machines of the plurality of approaching passengers. The method further comprises calculating, at the one or more detection system processors, a number of the approaching passengers proximate to the mass event from the passenger information. The method further comprises creating a demand forecast using demand forecasting modules that are attached to a mass event controller and subsequently sending direction messages from the one or more detection system processors to the mobile devices of the plurality of approaching passengers directing to one of a plurality of areas in the stop, depending on the demand forecast.

[0038] A computer-implemented method for management of a plurality of autonomous vehicles at a stop is also disclosed. The method comprises receiving, by one or more detection system processors, passenger information from one of mobile devices or ticket machines of the plurality of departing passengers. The passenger information comprises at least details of the stop at which the departing passenger wishes to board and a passenger identification. The method further comprises calculation at the one or more detection system processors of a number of the departing passengers proximate to the stop from the passenger information. In a next step, the method sends direction messages from the one or more detection system processors to one of the mobile devices of the plurality of departing passengers directing to one of a plurality of areas in the stop. Further the method comprises calling further autonomous vehicles to the stop, when a large number of the departing passengers is calculated.

[0039] It will be appreciated that the number of departing passengers should be forward calculated for a period of time to see whether the threshold value will be reached in the next period of time and pro-active direction of the departing passengers can be initiated even if the threshold value has not yet been reached. The number of departing passengers can be forward calculated by considering approaching passengers in a detection zone proximate to the stop, who have not yet arrived at the stop.

[0040] In a further aspect, the method sends direction messages from the one or more detection system processors to one of the mobile devices of the plurality of approaching passengers directing to different ones of the stops or an alternative one of a plurality of areas within the stop.

Description of the Figures

[0041] Fig. 1 shows an overview of the autonomous transportation network.

[0042] Fig. 2 shows an overview of a stop in the autonomous transportation network.

[0043] Fig. 3 shows a flow diagram of the boarding process.

[0044] Fig. 4 shows a flow diagram of the boarding process at a mass event.

Detailed Description of the Invention

[0045] The invention will now be described on the basis of the drawings. It will be understood that the embodiments and aspects of the invention described herein are only examples and do not limit the protective scope of the claims in any way. The invention is defined by the claims and their equivalents. It will be understood that features of one aspect or embodiment of the invention can be combined with a feature of a different aspect or aspects and/or embodiments of the invention.

[0046] Fig. 1 shows an example of an autonomous transportation network 10 according to such as that described in the applicant’s co-pending UK Patent Application No. 20003395.7, the details of which are incorporated by reference into this application.

[0047] The autonomous transportation network 10 has a plurality of autonomous vehicles 20 running on a plurality of tracks 15. The tracks 15 form a network of tracks over which the autonomous vehicles 20 are able to run. It will be appreciated that the tracks 15 may include guide rails, such as steel rails or concrete guidance elements, but could also comprise separated roadways. It is envisaged that the tracks 15 may be separate infrastructure or could also be incorporated into regular roadways and streets as long as sufficient safety measures are incorporated. The tracks 15 are provided with a plurality of beacons 17 (similar to rail balises) which monitor the progress of the autonomous vehicles 20 in the autonomous transportation network 10 and can also send signals 19 by wireless means to vehicles antennas 28 on the autonomous vehicles 20.

[0048] The autonomous vehicles 20 can be parked in a parking place with a plurality of tracks 15, be waiting at one or more stops 30 (shown in Fig. 2) or be in motion along the tracks 15. The autonomous vehicles 20 will be typically battery powered and can be charged, for example, when the autonomous vehicles 20 are in the parking places.

[0049] The autonomous transportation network 10 has a control management center 100 which monitors the progress of the autonomous vehicles 20 but does not directly control the progress of the autonomous vehicles 20. The autonomous vehicles 20 can send and receive information to the control management center 100, if necessary, and are connected to the control management center 100 through wireless connections using a vehicle antenna 25 located on the autonomous vehicle 20 in communication with the control management center 100 through the communications antenna 110 at the control management center 100. The control management center 100 is provided with a processor 120 and a central memory 140. The control management center 100 is connected to the beacons 17 using fixed communication lines 105 (although of course it would be possible to also use wireless connections over the distance between the beacons 17 and the control management center 100 or over part of the distance if required). The central memory 140 includes geographic data about the autonomous transportation network 10 including the location of the beacons 17.

[0050] A vehicle memory 25 in the autonomous vehicle 20 stores a geographic data in the form of a network map with the locations of the plurality of stops 30 and also a selection of pre-calculated routes along the tracks 15 between any two of the stops 30. There will generally be more than one pre-calculated route between two of the stops 30 to allow for alternative paths to be followed, if one of the pre-calculated routes is blocks.

[0051] The autonomous vehicle 20 has not only the afore-mentioned vehicle antenna 28 and the vehicle memory 25 but will also include an onboard processor 27 which can control the autonomous vehicle 20 using the information in the vehicle memory 25 and any information received from the beacons 17. [0052] The autonomous transportation network 10 is provided with a plurality of stops 30, as is known from a railway, tram or bus network, and which are shown in detail in Fig. 2.

[0053] Those people interacting with the autonomous transportation network 10 can be classified into one of four different groups according to the type of movement of the people in relation to the stops 30. People of category number one arrive with one of the autonomous vehicles 20 at one of the stops 30. People of category number two want to depart with one of the autonomous vehicles 20 from one of the stops 30. People of category number three are approaching one of the stops 30 (e.g., on foot) and wish to use the autonomous transportation network 10. People of category number four move away from one of the stops 30 (e.g., on foot) after they may have used the autonomous transportation network 10. People of category number two are hereinafter termed departing passengers 35. People of category number three are hereinafter termed approaching passengers 34.

[0054] The stops 30 will be clearly labelled to the approaching passengers 34 and the departing passengers 35. The approaching passengers 34 either book a ride through a mobile device 32, such as a smartphone, through a ticket machine 33 or indeed in person from a ticket seller. The approaching passenger 34 after booking the ride or purchasing their tickets become departing passengers 35 and will wait in one of a plurality of areas 37a-c at the stop 30. As noted elsewhere, after a mass event, both departing passengers 35 and approaching passengers 34 will be directed to a stop 30.

[0055] The stop 30 is shown in more detail in Fig. 2 and includes a detection system 50 for detecting the number of departing passengers 35 at the stop 30. This detection will be explained in more detail in connection with Fig. 3 and could be done in a number of ways.

For example, an antenna at the stop could detect using an open Bluetooth connection the number of mobile devices 32 at the stop or by forward calculation. A face or person visual detection system could be used or GPS localization of the mobile devices 32 could be analyzed.

[0056] The detection system 50 will include a detection system processor 52 processing the data as well as communication means 51 to receive and send messages 55 from and to mobile devices 32 held by the departing passengers 35. The communication with the mobile devices 32 can be done by any number of suitable protocols, such as but not limited to, GSM, UMTS, 3G, LTE, 4G, NFC, Bluetooth. In particular, the detection system 50 is able to calculate the number of departing passengers 35 at the stop 30 and, if the number of the departing passengers 35 exceeds a certain threshold value, then the detection system 50 is able to direct the departing passengers 35 to one of more of the areas 37a-c at the stop 30 to avoid too many departing passengers 35 congregating at any one position within the stop 30. The detection system 50 is also able to call further autonomous vehicles 20 to the stop when a large number of departing passengers 35 are expected.

[0057] The direction of the departing passengers 35 to different areas 37a-c also enables boarding of arriving ones of the autonomous vehicles 20 to be performed more efficiently. This direction of the departing passengers 35 leads to shorter boarding times into the autonomous vehicles 20 and therefore shorter travel times for the departing passengers 35. Congestion on the tracks 15 in the vicinity of a stop 30 is reduced, as the autonomous vehicles 20 do not have to wait that long to proceed to the stop due to the shorter boarding times. The direction of the departing passengers 35 to different areas 37a-c can also enable distribution of the arriving autonomous vehicles 20 to be managed to avoid congestion when deboarding passengers get off the arriving autonomous vehicles 20.

[0058] The areas 37a-c can be permanently marked on the surface of the stop 30 so that passengers or can be dynamically adjustable, for example using lighting 38. At some stops it may be advantageous for physical barriers 39 to be erected between the different ones of the areas 37a-c to manage the waiting passengers more efficiently.

[0059] The operation of the boarding of the autonomous vehicle 20 will now be described in connection with Fig. 3.

[0060] As mentioned above, the detection system 50 will include a processor 51 which can manage the method for boarding the departing passengers 35 into the autonomous vehicles 20 at the stop 30. The autonomous vehicles 20 may be already at the stop 30 (or in a nearby waiting area) or the autonomous vehicles 20 may be arriving without passengers or bringing deboarding passengers.

[0061] The method comprises in a first step 300 receiving in the detection system 50 by the afore-mentioned communication means passenger information 36 from the mobile devices 32 of the plurality of departing passengers 35. The detection system 50 could also receive the passenger information 36 for the ticket machines 33 or from devices used by other ticket sellers. The receiving of the passenger information 36 can be through mobile devices 32 carried by the departing passengers 35 or through the ticket machines 33 on purchase of a ticket. The received passenger information 36 will comprise passenger identification information and may also include any specific requirements, such as but not limited to the requested departure time, whether the departing passenger 35 is travelling with other departing passengers 35, with young children, or has a mobility impairment. In addition to the mobile devices 32, the ticket machines 33 from which the departing passenger 35 buys a ticket to a destination can provide the passenger information and any specific requirements for the passenger. The ticket machines 33 are also connected to the detection system 50 by wireless communication or by fixed communication lines (not shown).

[0062] In step 320, the number of the departing passengers 35 at the stop 30 is calculated by the detection system processor 52. This number is obtained from the received passenger information 36 of the plurality of departing passengers 35. In an additional aspect, the number of the departing passengers 35 at the stop 30 is forward calculated for the next period of time by the detection system processor 52. This number is obtained from the received passenger information 36 of those departing passengers together with the approaching passengers 34 who have not yet arrived at the stop 30 but are in a detection zone 31 proximate to the stop 30.

[0063] The forward calculation of the number of the departing passengers 35 can be obtained by calculating a demand forecast by using demand forecasting modules stored in a memory attached to the detection system processor 52. The demand forecast calculates an expected number of departing passengers 35 for the stop 30 during a forecast period. The demand forecast can include information on the expected destinations of the expected number of departing passengers 35. The demand forecast modules contain the data necessary for the calculation of the demand forecast. It will also be possible to include several parameters such as weather conditions, time of day, day of the week etc. in the calculation the demand forecast. The forward calculation in step 320 enables a forecast to be made of demand for the autonomous vehicles.

[0064] In an additional aspect, the detection system 50 can, at some stations, make the assumption that many of the people in the detection zone 31 will be approaching passengers 34 wishing to use the autonomous transportation network 10. This would be the case, for example, if the people in the detection zone 31 have the appropriate application stored on their mobile device 32. The detection system 50 assumes that these people may also be requiring an autonomous vehicle 20 within a short period of time and thus the detection system processor 52 can take these people into account when forward calculating the number of departing passengers 35 at the stop 30.

[0065] The application on the mobile device 32 may also store common routes for the approaching passengers 34 on the user devices 32 so that the detection system processor 52 in the detection system would also be able to know the probable destination of a potential customer, if required, before the potential customer checks into the autonomous transportation network 10. For example, a customer that regularly takes a route home from the office is likely to have this route stored in their user device 32 and the detection system processor 52 on detecting the presence of the mobile device 32 of the customer in the detection zone 31 can make a reasonable assumption that the customer will take the same route and be about to enter the stop 30 even if the customer has not checked into the network 10

[0066] In step 330, the detection system processor 52 calculates whether the number of the departing passengers 35 currently exceeds or is likely, in the next period of time, to exceed the threshold value. One typical, but not limiting value, would be seven departing passengers 35 present at the stop 30.

[0067] The departing passengers 35 will receive a direction message 55 indicating which stop 30 and/or which area 37a-c within the stop 30 at which the departing passenger 35 can board the autonomous vehicle 20. This direction message could be sent to the mobile device 32 or could be printed at the ticket machine 33. Generally, the direction message will inform the departing passenger 35 in step 335 to move to the closest area 37a-c as this is the quickest for the departing passenger 35 to reach and therefore the most efficient to reach.

[0068] Should the detection system processor 52 determine in step 330 that there are more than seven departing passengers 35 currently at the stop 30 - or likely to be at the stop 30 in the next period of time - then the detection system processor 52 will send direction messages 55 in step 340 from the detection system 50 using the communications means 51 to the mobile devices 32 of the plurality of departing passengers 35 (including possibly those in the detection zone 31) indicating an alternative boarding area 37a-c. These direction messages 55 could also be supplied via the ticketing machines 33. The direction messages 55 are used to direct the departing passengers 35 at the stop 30 and/or the approaching passengers 34 to a particular one of a plurality of areas 37a-c at the stop 30 to avoid overcrowding. The departing passengers 35 should move in both steps 335 and 340 towards the directed area 37a, 37b and 37c where the departing passengers 35 await the next arriving autonomous vehicle 20.

[0069] In a further aspect, the sending of ones of the direction messages 55 to ones of the departing passengers 35 can be delayed for a specific timespan, should the calculated number of the departing passengers 35 be too large with respect to the capacity of the stop 30. It can be avoided that too many of the autonomous vehicles 20 will arrive to pick up passengers at the stop 30 at the same time by distributing the departure times of the departing passengers 35 over an extended time period. The autonomous vehicles 20 will be controlled to arrive at the stop 30 corresponding to the distributed departure times. It will be appreciated that this

IB delay in sending the autonomous vehicles 20 to the stop 30 can potentially lead to longer waiting times for some of the departing passengers 35. However, the overall passenger capacity of the automated transport network 10 will be nevertheless increased, as congestions on the tracks in the vicinity of the stop 30 due to too many of the autonomous vehicles 20 arriving at the stop 30 simultaneously are avoided.

[0070] Within any given area 37a-c is fairly easy for the departing passengers 35 to arrange themselves informally in a line or queue to board the arriving autonomous vehicle 20 in an orderly manner. In addition, the departing passengers 35 are likely amongst themselves to prioritize passengers with mobility impairments or children to take the next arriving autonomous vehicle 20. The boarding commences in step 350 and the vehicle departs in step 370.

[0071] There are areas, such as at rail stations or outside stadiums, in which the simple method of boarding outlined above is not sufficient as this could lead to overcrowding of the stops 30. In this case, physical barriers 39 can be used to guide and separate the waiting passengers 35. The physical barriers 39 can also be used to separate alighting passengers getting off an arriving autonomous vehicle 20 from the departing passengers 35.

Furthermore, choke points 40 to prevent too many passengers 35 arriving at the boarding area 37 can be added. The choke points 40 shown in Fig. 2 are narrow entrances in a divider, but could be for example, escalators or travelling walkways. The direction messages 55 sent to the waiting passengers 35 and the approaching passengers 34 might further include guidance directions through one or more choke points 40 and/or alternate routings to the plurality of areas 37a-c to reduce congestion.

[0072] In a further aspect of the system, the method can be adjusted to cope with large numbers of approaching passengers 34 arriving after a mass event, such as a sports game or a rock concert. In this case, the system includes a mass event controller 60 connected to one or more of the detection system processors 52 at one or more stops 30 and sends the direction messages 55 to the plurality of departing passengers 35.

[0073] The mass event controller 60 does not necessarily take into account the threshold value of the number of the departing passengers 35 but calculates a demand forecast by using demand forecasting modules stored in a memory attached to the mass event controller 60.

The demand forecast calculates an expected number of departing passengers 35 for ones of the stops 30 during a forecast period. The demand forecast can include information on the expected destinations of the expected number of departing passengers 35. The demand forecast modules contain the data necessary for the calculation of the demand forecast. It will also be possible to include several parameters such as weather conditions, time of day, day of the week etc. in the calculation the demand forecast. The mass event controller 60 will direct the approaching passengers 34 and the departing passengers 35 from the mass event to one of the stops 30 and the area 37a-c within the stop to enable the mass event controller 60 and the detection system processors 52 to organize an efficient pick up of the departing passengers 35. This direction could include directing the passengers through alternate routings, including but not limited to choke points 40, to reach the stops 30 and/or the areas 37a-c if there is congestion on a direct or preferred route.

[0074] The mass event controller 60 could also be employed at transit hubs, such as rail stations, so that approaching passengers 34 alighting from a train would then be directed in a similar manner to one of a number of the stops and the areas 37a-c around the rail station for onward transportation to their final destination.

[0075] In one aspect of the invention, the mass event controller 60 and/or the detection systems are able to forecast demand for the autonomous vehicles and call up (in step 345) additional ones of the autonomous vehicles 20. It would be possible for the approaching passengers 34 to be directed to different ones of the stops 30 or the areas 37a-c to balance the load - this could even include being sent to a more distant stop 30 to board the autonomous vehicle 20 to ensure that the departing passenger 35 did not have to wait a long time.

[0076] The operation of the mass event controller is shown in Fig. 4. The method for boarding the autonomous vehicle comprises receiving in step 400, by one or more detection system processors at the stop 30, passenger information from one of mobile devices or ticket machines of the plurality of approaching passengers. In the next step 410, a number of the approaching passengers proximate to the mass event is calculated from the passenger information. In the next step 415, a forecast for the demand of the departing passengers 35 is calculated by the mass event controller 60. Finally in step 420, direction messages 55 are sent from one or more detection system processors to the mobile devices 32 of the plurality of approaching passengers 20 or the ticket machines directing to one of a plurality of areas 37a-c in the stop 30. Reference Numerals

10 Network

15 Tracks

17 Beacons

19 Signals

20 Autonomous vehicle

25 Vehicle memory

27 Onboard Processor

28 Vehicle antenna

30 Stop

31 Detection zone

32 Mobile devices

33 Ticket machine

34 Approaching passengers

35 Departing Passengers

36 Passenger information

37 Areas

38 Lighting

39 Physical Barriers

40 Choke Points/Device

50 Detection System

51 Communications means

52 Detection system Processor

55 Direction Messages

60 Mass event controller

65 Control signal

100 Control management center

105 Fixed communications lines

120 Processor

140 Central memory