COMP1110-Project-Report
File description: The project report for COMP1110 (25/26 Spring).
Document status: FIN
Last modified: 2026-09-11 21:12 HKT
FINAL REPORT FOR THE COURSE COMP1110 – GROUP D08
CHUN HEI BANH, SZE POK LIU, ZHAN HO JACOB SHING, YUBING YE, AND YIKUN ZHANG
Contents
1. Problem Definition and Modelling 1
1.1. Problem Definition 1
1.2. Problem Modelling 2
2. Surveying of Existing Solutions 3
3. Core Implementation 3
3.1. System Architecture Overview 3
3.2. Data Model 6
3.3. Data Collection, Representation, and Validation 6
3.4. Route Engine 7
3.5. User Interface 8
4. Scenario Study 9
4.1. Scenario 1 – Last-minute Arrival at Campus 9
4.2. Scenario 2 – Accessible Route Navigation 10
4.3. Scenario 3 – Multi-Stop Navigation 11
4.4. Scenario 4 – Stair-Preferring Navigation at Elevated Speed 13
4.5. Overall Findings 13
5. Evaluation, Limitations, and Possible Improvements 14
5.1. Evaluation 14
5.2. Limitations 14
5.3. Possible Improvements 15
References 15
Appendix A. Revised Task Assignment 16
1. Problem Definition and Modelling
1.1. Problem Definition. The University of Hong Kong (HKU) campus occupies a steeply terraced
hill in which buildings span multiple levels and are interconnected by a network of outdoor paths,
staircases, escalators, lifts, and covered walkways. For students and staff navigating this environment
daily, identifying the most suitable route between two locations is non-trivial. A student rushing from
the MTR station to a lecture theatre before a class begins, for example, must reason about path lengths,
vertical changes in elevation, and their own walking pace simultaneously. Similarly, a person with a
mobility impairment must identify routes that are entirely free of stairs and escalators — a constraint that
standard general-purpose mapping applications do not reliably enforce at the granularity of individual
campus segments.
The significance of the problem is beyond mere convenience. Arriving late to examinations or labora-
tory sessions has direct academic consequences, and the inability to find accessible routes may exclude
users from participating fully in campus life. Despite this, no dedicated pedestrian navigation tool tar-
geting HKU’s specific topology exists. General-purpose solutions such as Google Maps operate at a
coarser spatial granularity and do not model campus-internal infrastructure such as covered linkways or
building-internal lifts as distinct, traversable segments.
Date: 8 April 2026.
1
Page 1 of 16
2 C. BANH, S. LIU, J. SHING, Y. YE, AND Y. ZHANG
Several challenges compound the difficulty of the problem. First, the path network is heterogeneous:
segments differ not only in length but in type (flat walkway, staircase, escalator, lift), and each type
interacts differently with a pedestrian’s speed and physical capability. Second, user requirements are
multi-dimensional: a single query may simultaneously impose hard exclusion constraints (“no stairs”),
soft preferences (“prefer lifts”), and a personalised speed parameter. Third, between any pair of adjacent
locations there may exist multiple parallel segments of different types, forming a multigraph structure that
complicates standard shortest-path reasoning. Finally, routes may pass through one or more mandatory
intermediate waypoints, requiring the navigation problem to be solved as a sequence of coupled sub-
problems.
1.2. Problem Modelling.
1.2.1. Graph Representation. The campus is modelled as a finite, undirected multigraph
(1) G = (V, E),
where the vertex set V represents discrete locations of interest on campus (building entrances, floor
landings, transit exits, etc.), and the edge multiset E represents traversable segments connecting pairs
of locations. The multigraph formulation is necessary because two vertices may be connected by more
than one edge of distinct types — for instance, two adjacent floors of a building may be linked by both
a staircase and a lift, each forming a separate edge.
Each edge e ∈ E carries two attributes: a type τ (e) ∈ T and a base traversal time c(e) ∈ Z
>0
measured in seconds. The type set is
(2) T = { Flat, Stairs, MinorStairs, Escalator, Lift },
reflecting the physical modes of traversal present on campus, where MinorStairs denotes flights of
at most 7 steps (including the top landing but excluding the ground-level step), and other types are
self-explanatory.
1.2.2. Speed-Adjusted Edge Costs. Pedestrian speed varies between individuals and circumstances. To
model this, a scalar speed multiplier σ > 0 is supplied by the user, where σ = 1 corresponds to an average
walking pace, σ > 1 to a faster pace, and σ < 1 to a slower one.
The degree to which speed affects traversal time depends on the edge type. Mechanical conveyances
such as lifts operate at a fixed speed independent of the passenger, while climbing a staircase is far more
sensitive to physical exertion than walking on flat ground. This is captured by a type-dependent speed
exponent α : T → R
≥0
, defined as
(3) α(τ ) =
⎧
⎪
⎪
⎪
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎪
⎪
⎪
⎩
1.0 τ = Flat,
1.3 τ = MinorStairs,
1.5 τ = Stairs,
0.3 τ = Escalator,
0.0 τ = Lift.
The adjusted cost of traversing an edge e at speed multiplier σ is then
(4) ˆc(e, σ) =
c(e)
σ
α(τ (e))
.
For α(τ) = 0 (lifts), the adjusted cost equals the base cost regardless of σ. For α(τ) = 1.5 (stairs), the
adjusted cost decreases more steeply as σ increases, reflecting the disproportionate benefit of a faster
pace on elevation gain.
1.2.3. Preference-Weighted Cost Function. Users may specify a preference profile P , which partitions T
into three disjoint subsets: T
avoid
(P ) (hard exclusions), T
prefer
(P ) (soft preferences), and T
neutral
(P ) (no
preference). A fixed preference discount factor β ∈ (0, 1) rewards the use of preferred segment types by
reducing their effective cost. The pathfinding cost function w : E → R
≥0
∪ {∞} is defined as
(5) w(e, σ, P ) =
⎧
⎪
⎨
⎪
⎩
∞ τ (e) ∈ T
avoid
(P ),
β · ˆc(e, σ) τ(e) ∈ T
prefer
(P ),
ˆc(e, σ) otherwise.
Edges with w = ∞ are effectively removed from the graph during the search, enforcing hard exclusion
constraints absolutely.
Page 2 of 16
FINAL REPORT 3
1.2.4. Route and Multi-Waypoint Formulation. A route is a walk π = (v
0
, e
1
, v
1
, . . . , e
k
, v
k
) in G. Its
total weighted cost is
(6) W (π) =
k
i=1
w(e
i
, σ, P ),
and its total displayed travel time is computed using the adjusted costs ˆc (without the preference dis-
count), so that the displayed time reflects true traversal duration rather than the artificial cost used to
bias routing.
When the user specifies m ≥ 1 intermediate waypoints in addition to origin and destination, the
full journey is decomposed into m + 1 independent sub-problems. Given an ordered waypoint sequence
(w
0
, w
1
, . . . , w
m+1
), the optimal route for each segment [w
i
, w
i+1
] is found independently, and the full
route is their concatenation.
1.2.5. Multi-Candidate Route Generation. Rather than returning a single optimal route, the system
generates a small set of candidate routes by solving the shortest-path problem under several distinct
preference profiles derived from the user’s input (for example, one enforcing stair avoidance and another
promoting lift usage). Duplicate routes, identified by equality of their ordered edge sequences, are
discarded. The surviving candidates are then ranked and labelled according to secondary criteria such
as total travel time and number of stops.
2. Surveying of Existing Solutions
As planned, six existing solutions for navigation, grouped into three categories, were surveyed and
evaluated on different aspects. The solutions are first compared in an overall manner in Table 1, and
then compared in a more detailed manner with focus on specific features in Table 2.
The three categories of solutions surveyed are: (1) general-purpose navigation applications, repre-
sented by Google Maps and Citymapper; (2) transit-specific applications, represented by MTR Mobile
and HKBUS.app; and (3) static campus reference tools, represented by the HKU campus map and on-
campus signage. The general-purpose navigation applications provide dynamic estimated times of arrival,
multi-modal transfer handling, and city-scale route planning, but both perform poorly in multi-floor en-
vironments such as the HKU campus and operate exclusively online. The transit-specific applications
are limited in scope to their respective transport modes: MTR Mobile supports only station-to-station
queries within the MTR and light rail network, while HKBUS.app restricts users to at most one bus
transfer and cannot locate the user or direct them to the nearest bus stop. Both transit apps also require
a live network connection. The static campus reference tools — the HKU campus map and on-campus
signage — provide a visual overview of building locations and nearby paths, but are non-interactive,
offer no route direction or estimated arrival time, and are inherently unable to adapt to a user’s starting
point or destination.
The surveying exercise informed several concrete design decisions for this project. The station-based
topology of transit apps, in which discrete named stops are connected by typed transport links, was
adopted as the foundational data model, resulting in the node-and-edge multigraph representation de-
scribed in subsection 1.2. The accessibility mode offered by Google Maps — which avoids stairs and
promotes lift usage — motivated the Accessible Route preference toggle and the hard-exclusion mecha-
nism in the cost function. The route suggestion display of Citymapper, which presents fare and expected
time for each option in a compact list, informed the design of the route card display, where each candidate
route is accompanied by a time estimate, stop count, and a salient label indicating its primary charac-
teristic. Finally, the dependence of all surveyed online solutions on live network connectivity, contrasted
with the offline availability of the HKU campus map PDF, motivated the decision to bundle a static copy
of the campus dataset within the application, ensuring full functionality in offline or restricted-network
environments.
3. Core Implementation
3.1. System Architecture Overview. The project is constructed with a layered and modular ar-
chitecture, as illustrated in Figure 1. The project enforces principles of object-oriented programming
throughout its design, with some utility functions implemented in a functional style to avoid boilerplate
code and improve readability. The design allows a clear separation of data representation, data parsing,
route-finding business logic, and state management of the user interface, which facilitates maintainability
of the codebase.
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4 C. BANH, S. LIU, J. SHING, Y. YE, AND Y. ZHANG
Table 1: Overall comparison of the existing solutions.
Solution
Aspect
Target Users Key Features Missing Features Assumptions
Google Maps Any users, especially users
who are trying to find a
location that is on the
street.
Provide a real-time
instruction from the current
location to the destination.
Poor performance in
multiplicity floors, such as
the HKU campus or a mall.
None.
Citymapper Users who prefer to direct
them from one place to
another.
Provide suggestions
displayed with fare and
expected time in a list.
Poor translation due to the
software being originally in
English.
None.
MTR Mobile Users who want to travel
by MTR.
Provide the estimated time
of the train and its
start/end service times.
Only station-to-station, but
not available to handle
arbitrary locations.
Assume the user only takes
MTR and travels from
station to station.
HKBUS.app Users who want to take a
bus or a minibus.
Provide the estimated
waiting time for the next
bus.
Unable to locate where you
are and direct you to the
bus station.
Assume users only take the
bus instead of other modes
of transportation, such as
the MTR.
HKU campus map Users who may be
interested in HKU or will
go to HKU later.
Provide a map for building
on campus with names
labeled.
Static map and unable to
show 3D structure.
None.
On-campus signage Users on campus who are
finding a building.
Show where the signs are
and the nearby path and
building on campus.
Static map, unable to
direct the user to where
they want.
Assume users will only
travel within campus.
Page 4 of 16
FINAL REPORT 5
Table 2: Comparison of existing solutions on specific aspects.
Solution
Aspect
Estimated Time of
Arrival (ETA)
Fair Display Transfer Handling Accessibility Info Offline Availability
Google Maps Provided ETA with
multiple methods,
calculated
dynamically with the
current situation.
No fare display. Able to handle any
transfer, even if some
distance needs to be
walked.
Provided accessibility
mode to avoid stairs
and prioritize the lift.
No route planning
function when offline.
Citymapper Provided ETA with
multiple methods.
Provided adult fares
for multiple methods.
Taxi fare is estimated
by driving time.
Able to handle any
transfer, even if some
distance needs to be
walked.
No accessibility info. Unable to use it
offline.
MTR Mobile Provided ETA with
multiple methods,
estimated by
maximum waiting
time.
Adult fare is
displayed, while
others can be
checked.
Able to handle any
transfer, only within
MTR stations or
light rail.
Able to display
accessibility facilities
for multiple kinds of
accessibility needs,
such as those of the
visually impaired.
No route planning
function when offline.
HKBUS.app ETA must be checked
by checking different
paths one by one.
Adult fare is
displayed separately
for different paths,
and you need to sum
them up to get a
total.
At most one transfer
is allowed. Able to
handle at most 500m
walking distance.
No accessibility info. Unable to use it
offline.
HKU campus map No ETA provided. No fare display. No transfer handling. No accessibility info. The web version is
unavailable offline,
but the PDF version
can be used offline.
On-campus signage No ETA provided. No fare display. No transfer handling. No accessibility info. Not applicable.
Page 5 of 16
6 C. BANH, S. LIU, J. SHING, Y. YE, AND Y. ZHANG
As for the technical stack, the project is implemented in Python 3.13, with its terminal user interface
built using the Textual[?textual] library.
Data Model
Routing Engine UI Components
Figure 1. System architecture diagram.
3.2. Data Model. The entities required by Equation 1 are represented by three classes: Node (v),
Edge (e), and CampusMap (G). The core attributes and their relationships are illustrated in Figure 2.
Each class comes with a set of methods for constructing instances, accessing attributes, and performing
relevant operations. These classes together represent a undirected multigraph, which allows multiple
edges of different edge types between the same pair of nodes.
CampusMap
- nodes : dict[str, Node]
- edges : list[Edge]
Node
+ node id : str
+ description : str
- edges : list[Edge]
Edge
+ node1 : Node
+ node2 : Node
+ edge type : EdgeType
+ time cost : int
≪enumeration≫
EdgeType
FLAT
STAIRS
MINOR STAIRS
ESCALATOR
LIFT
1
0..∗
1
0..∗
references uses
Figure 2. Class diagram of the data model.
3.3. Data Collection, Representation, and Validation. For implementing this project, the group
has divided the campus into 4 zones for distributing labour. Each zone is assigned to a member, who is
responsible for defining the nodes within the zone. After finalising the nodes, the member will then walk
between the nodes and measure the time taken to traverse each edge (measured in seconds) and record
the data on a shared Google Sheet document. One of the members is responsible for measuring the
time taken between nodes that fall in different zones, and the data is recorded in the same Google Sheet
document. To ensure consistency and reliability of the data, the group has agreed on a standardised
method for measuring traversal times and an acceptable range of walking paces.
The data on Google Sheets is fetched automatically by a GitHub Action workflow daily. The work-
flow calls fetch csv from google sheet() defined in campus nav/utils.py. This function converts
the spreadsheet data into a CSV format. The two CSV files are campus nav/data/nodes.csv and
campus nav/data/edges.csv. Their schemas are defined in Listing 1 and Listing 2, respectively.
In particular, to make the nodes and edges be more consistent, they are defined in a way that all of
the entries can be deterministically sorted. Nodes are sorted by their node id in lexicographical order.
For edges, in the normalisation process, the two incident nodes are compared, such that node1 is always
lexicographically sorted before node2. Then, the edges are sorted by the tuple (node1, node2, edge type,
time cost) in lexicographical/numerical order. The serialisation codes enforce these sorting rules.
Page 6 of 16
FINAL REPORT 7
Listing 1. CSV Schema for nodes.csv
1 version 1.1
2 @totalColumns 2
3
4 "node_id": notEmpty unique
5 "node_description":
Listing 2. CSV Schema for edges.csv
1 version 1.1
2 @totalColumns 4
3
4 "node_1_id": notEmpty
5 "node_2_id": notEmpty
6 "edge_type": notEmpty is("FLAT", "STAIRS", "MINOR_STAIRS", "ESCALATOR", "LIFT")
7 "time_cost": notEmpty positiveInteger
3.4. Route Engine. The route engine is the core of the navigation system. Given a campus map,
an ordered list of waypoints, a set of user preferences, and a speed multiplier, it is responsible for
computing a set of candidate routes for the user to choose from. The engine is implemented entirely in
campus nav/main app/route engine.py as a collection of pure functions, with no dependency on any
UI component.
3.4.1. Representing User Preferences. User preferences are encapsulated in the Preferences dataclass,
whose fields correspond directly to the options exposed in the configuration screen. Each field is a
boolean flag, grouped into three pairs: avoid/prefer stairs, avoid/prefer escalators, and avoid/prefer lifts.
A separate accessible flag acts as a master toggle that implicitly enables stair and escalator avoidance
and lift preference.
3.4.2. Computing Edge Costs. The cost assigned to an edge during pathfinding implements the model
described in the Problem Modelling section. First, the base traversal time c(e) is adjusted for the user’s
walking speed using Equation 4, where σ is the speed multiplier and α(τ) is the type-dependent speed
exponent defined in Equation 3. This is implemented directly in compute adjusted cost() (Listing 3).
Listing 3. Speed-adjusted edge cost computation
1 _SPEED_EXPONENTS = {
2 EdgeType.FLAT: 1.0,
3 EdgeType.STAIRS: 1.5,
4 EdgeType.MINOR_STAIRS: 1.3,
5 EdgeType.ESCALATOR: 0.3,
6 EdgeType.LIFT: 0.0,
7 }
8
9 def compute_adjusted_cost(edge, speed_multiplier):
10 exponent = _SPEED_EXPONENTS[edge.edge_type]
11 return edge.time_cost / (speed_multiplier ** exponent)
The preference-weighted cost function w(e, σ, P ) from Equation 5 is then applied on top. Hard avoid-
ance constraints are enforced by returning infinity for the forbidden edge types, which effectively removes
them from consideration. Soft preferences reduce the cost of favoured edge types by a factor of β = 0.5,
making them appear cheaper to the pathfinding algorithm and therefore more likely to be included in
the result.
3.4.3. Pathfinding with Dijkstra’s Algorithm. The dijkstra() function finds the lowest-cost path be-
tween two nodes in the campus map. It implements Dijkstra’s algorithm, a classic and well-known
method for finding the shortest path in a graph with non-negative edge weights.
Page 7 of 16
8 C. BANH, S. LIU, J. SHING, Y. YE, AND Y. ZHANG
The algorithm works by maintaining a priority queue. It starts at the source node with a cost of zero,
and repeatedly picks the unvisited node with the lowest known cost so far. For each such node, it checks
all its neighbouring edges: if travelling via the current node would reach a neighbour at a lower cost than
previously known, the neighbour’s cost is updated and it is added to the priority queue. This process
repeats until the destination node is reached. The path is then reconstructed by tracing back through a
prev dictionary, which records how each node was reached.
Listing 4. Core loop of Dijkstra’s algorithm
1 while pq:
2 d, u = heapq.heappop(pq) # pick lowest-cost node
3 if d > dist[u]:
4 continue # skip if already processed
5 if u == end_id:
6 break # reached destination
7 for edge in nodes[u].get_edges():
8 v = edge.get_other_node_of(nodes[u]).node_id
9 c = cost_fn(edge)
10 if c >= _INF:
11 continue # skip forbidden edges
12 if d + c < dist[v]:
13 dist[v] = d + c
14 prev[v] = (u, edge)
15 heapq.heappush(pq, (d + c, v))
The cost function is passed in as an argument (cost fn), which allows the same algorithm to be
reused for different routing strategies without any modification.
3.4.4. Multi-Waypoint Support. When the user specifies intermediate waypoints, the route is decomposed
into segments as described in the Problem Modelling section. The function find route() iterates over
each consecutive pair of waypoints and runs Dijkstra’s algorithm independently for each segment. The
resulting steps are concatenated to form the complete route, and the total adjusted cost is computed as
the sum of the adjusted costs of all steps.
3.4.5. Multi-Strategy Candidate Generation. Rather than returning a single route, find candidate routes()
runs the pathfinding algorithm under four different preference configurations to produce a diverse set of
options:
(1) The user’s own preferences, as configured.
(2) A variant with stair avoidance forced on, encouraging the use of lifts and escalators.
(3) A variant with lift preference forced on, favouring lift-equipped paths.
(4) A fewest-stops variant, where all passable edges are assigned a uniform cost of 1 regardless of
traversal time, so that Dijkstra minimises the number of hops rather than total time.
After running all four strategies, any duplicate routes — identified by comparing their full ordered
sequences of edges — are discarded. The surviving unique routes are then labelled based on their
properties: the route with the lowest total time receives the Fastest label; the route with the fewest steps
receives the Fewest Stops label; and routes are additionally tagged to indicate whether they contain
stairs, lifts, or escalators.
3.5. User Interface. The user interface is implemented using the Textual library, which provides a
framework for building terminal-based applications with a modern, responsive design. The app launches
into an AppSelector, where the user can choose to launch the navigation app or the data visualisation
app. The navigation app is where the main functionality of the project is implemented, and it is
orchestrated by several screens, each focusing on a specific task. The data visualisation app is only a
helper for development and is not part of this project. The flow of the user interface is illustrated in
Figure 3.
Note that the data visualisation app requires a graphical environment to run and will fail if launched
in a headless terminal (e.g., via SSH on HKU’s Ubuntu servers).
Page 8 of 16
FINAL REPORT 9
Navigator AppData Visualiser (helper)
Start
Exit
AppSelector
Select an option
User choice?
MainApp
Load campus map from CSV
ConfigurationScreen
Set waypoints, preferences, speed
WaypointSelection-
Screen
Search & pick a node
SolutionsScreen
Display route cards
RouteDetailsScreen
Step-by-step view
DataVisualiserApp
TUI launcher
Spawn GUI subprocess
(PySide6 + Sigma.js)
GUI running
Health-check polling loop
Exit
Navigator
Visualiser
Add/Edit
Confirm / Cancel
Go
Details
Back
Back
Quit
GUI process exited
Figure 3. User interface flow diagram.
4. Scenario Study
For the sake of evaluating the performance of the product, several scenarios were designed to test the
product’s functionality and the quality of the results it produces. These scenarios, together with the
sample input, output, and evaluation of the results, are presented in this section. All of the scenarios
are tested on the same network graph.
4.1. Scenario 1 – Last-minute Arrival at Campus. This scenario simulates a situation where a
student arrives at the campus last minute just before the start of a test.
4.1.1. Scenario Assumptions. It is assumed that the student arrives at the MTR A2 exit at 10:55 am,
and needs to get to the classroom at 1/F of the Meng Wah Complex before 11:00 am.
4.1.2. Purpose of the Scenario. The purpose of this scenario is to evaluate the system’s ability to provide
accurate and efficient navigation solutions under time pressure.
4.1.3. Inputs. The following parameters are input into the system (all other parameters are left as de-
fault):
• Starting Node: UStr Upper MTR Exit
• Destination Node: MengWahComplex 1F
• Speed Multiplier: 1.3 (to simulate the student rushing to the classroom with a faster pace)
4.1.4. Outputs. The system generated three solutions for the scenario, which are presented in Figure 4.
4.1.5. Discussions. For this scenario, one of the main concerns is the time taken to reach the destination.
As the student is in a rush, it would be reasonable to expect that the student would walk faster than
usual. Existing pedestrian navigation solutions typically assume a normal walking pace and do not
account for variations. They tend to estimate the arrival time as the user walks using past data. While
this approach may work for later sections of the trail, it is prone to inaccuracies at the beginning. In
Page 9 of 16
10 C. BANH, S. LIU, J. SHING, Y. YE, AND Y. ZHANG
UStr Upper MTR Exit
UStr Upper InnoWing Entrance
StephenH GeoMuseum Entrance
InnoWingTwo
RunRunSBuilding GF Entrance
RaysonHTheatre GF
RunmeS GF Entrance
RunmeS 4F
MengWahComplex 1F
CYMAmenitiesCtr 2F Entrance
CYMAmenitiesCtr 4F Entrance
MengWahComplex GF
flat; 58s
flat; 5s
flat; 29s
flat; 17s
flat; 28s
flat; 6s
stairs; 59s
flat; 22s
flat; 58s
flat; 5s
flat; 29s
flat; 17s
flat; 28s
flat; 6s
flat; 21s
escalator; 34s
lift; 23s
escalator; 23s
flat; 58s
flat; 5s
flat; 29s
flat; 17s
flat; 28s
flat; 6s
lift; 1m 56s
flat; 22s
Solution 1
Fastest, 3m 44s
Solution 2
4m 38s, no stairs
Solution 3
4m 41s, no stairs
Figure 4. Output solutions for Scenario 1.
contrast, our project accounts for a customisable speed multiplier, which allows the user to adjust the
estimated time based on their current pace.
4.2. Scenario 2 – Accessible Route Navigation. This scenario simulates a user with mobility im-
pairment who needs to navigate across campus while avoiding all stairs and escalators.
4.2.1. Scenario Assumptions. It is assumed that the user arrives at the MTR C1 exit and needs to reach
the K.K. Leung Building ground floor entrance. The user cannot use stairs or escalators due to mobility
constraints and therefore requires a fully lift-accessible route. The user’s walking pace is assumed to be
slower than average.
4.2.2. Purpose of the Scenario. The purpose of this scenario is to evaluate the system’s ability to gen-
erate accessible routes that strictly exclude stairs and escalators while favouring lift-equipped segments.
This tests whether the Accessible Route preference correctly propagates the appropriate constraints
throughout the route-finding process.
4.2.3. Inputs. The following parameters are input into the system (all other parameters are left as de-
fault):
• Starting Node: MTR C1 Exit
• Destination Node: KKLeung Building GF
• Accessible Route: enabled (implicitly sets Avoid Stairs, Avoid Escalators, and Prefer Lifts)
• Speed Multiplier: 0.7 (to simulate a slower walking pace)
4.2.4. Outputs. The system generated two solutions for the scenario, which are presented in Figure 5.
Page 10 of 16
FINAL REPORT 11
MTR C1 Exit
RedWall
JockeyClubTower Lift GF
JockeyClubTower Lift LGF
ChowYeiChingBuilding LGF
CompositeBuilding GF
SURestaurant
HakingWong Lift Inside 4F
HakingWong Lift Outside 4F
UStr Lower InnoWing Terminal
UStr Upper LiftLobby NearInnoWing
MainLib 2F Entrance
MainLib GF Entrance
SunYatSenPlace HappyPark
Knowles Building
KKLeung Building GF
Lift Between KKLeung Knowles
ChiWah Staircase
ChowYeiChingBuilding GF
CompositeBuilding 1F
UStr Upper MTR Exit
UStr Upper InnoWing Entrance
LiftLobby NearCoffeeAcademics
lift; 2m 48s
flat; 1m 7s
lift; 10s
flat; 1m 40s
flat; 51s
flat; 1m 11s
flat; 7s
flat; 44s
flat; 1m 29s
lift; 9s
flat; 53s
lift; 53s
flat; 16s
flat; 37s
flat; 1m 1s
flat; 1m 27s
flat; 1m 44s
flat; 1m 47s
flat; 29s
lift; 43s
flat; 1m 23s
flat; 36s
flat; 1m 4s
flat; 29s
flat; 1m 34s
Solution 1
Fastest, No Stairs; 14m 27s
Solution 2
Fewest Stops, No Stairs; 15m 17s
Figure 5. Output solutions for Scenario 2.
4.2.5. Discussions. For this scenario, the main concern is ensuring a barrier-free path for a user with
mobility constraints. Since the user must avoid all stairs and escalators, it is reasonable to assume they
may at a slower pace than average. Existing pedestrian navigation solutions often prioritise the shortest
geometric distance and frequently overlook specific accessibility barriers for users with slower walking
speeds. In contrast, our project includes an “Accessible Route” preference that strictly avoid escalator-
required route and utilises a customisable speed multiplier to provide an accurate time estimate tailored
to the user’s actual pace.
4.3. Scenario 3 – Multi-Stop Navigation. This scenario simulates a student who needs to visit an
intermediate location on the way to their final destination, requiring the system to plan a route through
multiple specified waypoints.
4.3.1. Scenario Assumptions. It is assumed that the student starts at the Composite Building ground
floor and must pass through the Main Library ground floor entrance before proceeding to the Tang Chi
Ngong Building as the final destination. No special mobility preferences are set.
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12 C. BANH, S. LIU, J. SHING, Y. YE, AND Y. ZHANG
4.3.2. Purpose of the Scenario. The purpose of this scenario is to evaluate the system’s multi-waypoint
route planning capability. Specifically, it tests whether the system correctly chains route segments across
multiple stops and produces solutions that respect all intermediate waypoints in the correct order.
4.3.3. Inputs. The following parameters are input into the system (all other parameters are left as de-
fault):
• Starting Node: CompositeBuilding GF
• Via (Intermediate Stop): MainLib GF Entrance
• Destination Node: TangChiNgong Building
4.3.4. Outputs. The system generated three solutions for the scenario, which are presented in Figure 6.
CompositeBuilding GF
SURestaurant
HakingWong Lift Inside 4F
HakingWong Lift Outside 4F
UStr Lower InnoWing Terminal
UStr Upper LiftLobby NearInnoWing
CompositeBuilding 1F
UStr Upper MTR Exit
UStr Upper InnoWing Entrance
MainLib 2F Entrance
MainLib GF Entrance
SunYatSenPlace HappyPark
Knowles Building
Lift Between KKLeung Knowles
KKLeung Building GF
KKLeung Building LG2F
Swire Building
TangChiNgong Building
lift; 38s
flat; 1m 13s
flat; 1m 15s
flat; 20s
flat; 50s
flat; 50s
flat; 5sflat; 5s
flat; 31sflat; 31s
flat; 1m 2sflat; 1m 2s
lift; 9slift; 9s
flat; 37sflat; 37s flat; 37s
lift; 53s
lift; 53s lift; 53s
flat; 11s
flat; 11s flat; 11s
m.stairs; 17sflat; 25s flat; 25s
flat; 45s
flat; 45s
flat; 20s
flat; 20s
flat; 1m 6s
lift; 44slift; 44s lift; 44s
flat; 41sflat; 41s flat; 41s
flat; 56s
flat; 56s flat; 56s
Solution 1
Fastest; 8m 1s
Solution 2
No Stairs; 8m 9s
Solution 3
Fewest Stops, No Stairs; 8m 59s
Figure 6. Output solutions for Scenario 3.
Page 12 of 16
FINAL REPORT 13
4.3.5. Discussions. For this scenario, the primary focus is the system’s ability to plan a cohesive route
through multiple intermediate waypoints. As students often need to visit locations like the library before
heading to a classroom, the navigation must account for specific stops in a precise order. Existing
pedestrian navigation solutions typically treat multi-stop navigation as a series of independent, point-to-
point trips, which can lead to inefficient routing at the transition nodes. In contrast, our project features
a multi-waypoint planning capability that correctly chains route segments across all stops, ensuring the
system produces solutions that respect the user’s specific sequence of destinations while maintaining
overall efficiency.
4.4. Scenario 4 – Stair-Preferring Navigation at Elevated Speed. This scenario simulates a
physically active user who explicitly prefers routes that include staircases and walks at a faster-than-
normal pace.
4.4.1. Scenario Assumptions. It is assumed that the user starts at the MTR Upper University Street
exit and needs to reach the Meng Wah Complex 1/F. The user chooses to use stairs wherever possible,
for example to incorporate physical activity into their commute, and walks at a pace 1.5 times faster
than average.
4.4.2. Purpose of the Scenario. The purpose of this scenario is twofold. First, it evaluates whether the
Prefer Stairs option successfully biases the route engine towards stair-inclusive segments. Second, it
assesses whether an elevated speed multiplier correctly reduces estimated travel times, particularly for
stair segments which are modelled with a higher speed exponent than flat segments.
4.4.3. Inputs. The following parameters are input into the system (all other parameters are left as de-
fault):
• Starting Node: UStr
Upper MTR Exit
• Destination Node: MengWahComplex 1F
• Prefer Stairs: enabled
• Speed Multiplier: 1.5 (to simulate a faster walking pace)
4.4.4. Outputs. The system generated three solutions for the scenario, which are presented in Figure 7.
4.4.5. Discussions. For this scenario, the main concern is catering to a user who explicitly prefers stairs
and maintains a fast pace. As the user incorporates physical activity into their commute, it is reasonable
to expect they would walk significantly faster than usual. Existing pedestrian navigation solutions
typically assume a preference for flat terrain and a normal pace. They tend to default to the easiest
route and neglect specific requirements from users. In contrast, our project provides a “Prefer Stairs”
option and an elevated speed multiplier to accurately reflect travel times for active users.
However, as displayed on the output solutions, the system has included two other routes that contain
no stairs. While this is an intended behaviour as the “Prefer” preference profile only biases the route
search algorithm rather than enforcing a hard constraint, it may be confusing for users who expect
solutions with stairs. For future improvement, a visual prompt that reminds users of the possibility of
non-stair routes may be added to the output page to clarify the situation.
4.5. Overall Findings. Across all four scenarios, the system demonstrated a consistent ability to accom-
modate user-specific preferences and constraints that are seldom addressed by general-purpose navigation
applications. The preference system proved effective in producing routes tailored to diverse user needs.
The speed multiplier further distinguished the system from conventional navigation tools by enabling
personalised travel time estimates that reflect individual walking pace rather than relying on population-
level averages. The multi-waypoint capability was likewise validated, with the system correctly chaining
route segments through ordered intermediate stops while preserving overall route coherence.
Nevertheless, certain limitations were identified during testing. In more complex routing configura-
tions, the system occasionally produced routes containing repeated segments, resulting in unnecessary
detours. This issue arises from the segment-wise chaining strategy employed for multi-waypoint routes,
where locally optimal sub-routes may share edges when concatenated. Addressing this limitation remains
an area for future improvement, and would bring the system’s behaviour closer to that expected of a
production-grade pedestrian navigation tool.
Page 13 of 16
14 C. BANH, S. LIU, J. SHING, Y. YE, AND Y. ZHANG
UStr Upper MTR Exit
UStr Upper InnoWing Entrance
StephenH GeoMuseum Entrance
InnoWingTwo
RunRunSBuilding GF Entrance
RaysonHTheatre GF
RunmeS GF Entrance
RunmeS 4F
CYMAmenitiesCtr 2F Entrance
CYMAmenitiesCtr 4F Entrance
MengWahComplex 1F
MengWahComplex GF
flat; 50s
flat; 5s
flat; 25s
flat; 15s
flat; 24s
flat; 5s
stairs; 48s
lift; 1m 56s
flat; 19s
flat; 19s
flat; 18s
escalator; 33s
lift; 57s
escalator; 22s
Solution 1
Fastest, Has Stairs; 3m 11s
Solution 2
No Stairs; 4m 14s
Solution 3
Fewest Stops, No Stairs; 4m 19s
Figure 7. Output solutions for Scenario 4.
5. Evaluation, Limitations, and Possible Improvements
5.1. Evaluation. The system was evaluated through a structured set of ten test cases, organised into
three categories: basic path planning (TC-01 to TC-03), preferences and configuration (TC-04 to TC-07),
and boundary and exception handling (TC-08 to TC-10). The test cases cover a representative range
of user interactions, including normal shortest-path queries, multi-waypoint routes, the accessible route
mode, the speed multiplier, extreme input values, and conflicting or unsatisfiable preference configura-
tions. All ten test cases passed, with actual outputs matching expected behaviour in each case. The full
test case descriptions, inputs, expected outputs, actual results, and screenshots are documented in the
project’s README.md.
5.2. Limitations. Despite the passing test results, several limitations of the current system were iden-
tified.
5.2.1. Errors in Traversal Time Measurement. The base traversal times stored in edges.csv were col-
lected by group members physically walking each segment and measuring the elapsed time. This process
introduces unavoidable human variability: different members walked at different natural paces, and even
a single member’s pace varies between measurements depending on fatigue, crowd density at the time of
measurement, and the precision of the timing method used. As a result, the base traversal times may not
be internally consistent across the dataset, and any route cost computed from them carries an inherent
margin of error.
5.2.2. Insufficient Data Variation for Route Diversity. The multi-strategy candidate generation was de-
signed to return up to four distinct routes per query by running the pathfinding algorithm under different
preference profiles. In practice, however, the campus graph is relatively sparse in some areas, meaning
that many pairs of nodes have only one or two distinct paths between them regardless of the preference
Page 14 of 16
FINAL REPORT 15
profile applied. When this occurs, the deduplication step discards the redundant candidates, and fewer
routes are returned than expected. Users are therefore sometimes presented with only one or two options
rather than a full set of alternatives, which limits the usefulness of the multi-candidate design in those
parts of the campus.
5.2.3. No Support for Real-Time Infrastructure Status. The system has no mechanism to account for
infrastructure that is temporarily out of service. Lifts and escalators on the HKU campus are periodically
shut down for maintenance, and certain paths may be closed during construction or special events.
Because the graph is a static snapshot loaded from a CSV file, such closures cannot be reflected at
runtime. A user who has been routed through a lift that is currently under maintenance will only
discover the problem upon arrival, which is precisely the kind of friction the system was intended to
eliminate.
5.2.4. Route Repetition in Multi-Waypoint Queries. As noted in the Scenario Study section, the multi-
waypoint routing strategy decomposes the journey into independent segment-wise sub-problems. Because
each segment is optimised independently, the optimal path for one segment may share edges with the
optimal path for an adjacent segment, resulting in the user being directed to retrace steps they have
already walked. This is a structural limitation of the greedy chaining approach and cannot be resolved
without a more sophisticated global optimisation strategy.
5.2.5. No Consideration of Time-of-Day or Crowd Conditions. The traversal times in the dataset reflect
measurements taken at a particular time and under particular crowd conditions. In reality, heavily
trafficked corridors during peak hours (such as just before or after lectures) may take significantly longer
to traverse than the recorded values suggest. The system currently has no way to model time-dependent
edge weights.
5.3. Possible Improvements. Several directions for future improvement follow naturally from the
limitations above.
5.3.1. Standardised Data Collection Protocol. A more rigorous data collection protocol — for example,
requiring each segment to be walked by at least two members independently, with the recorded time
taken as the average — would reduce inter-member variability and improve the internal consistency of
the base traversal times. Alternatively, a calibration pass in which a single reference member re-walks a
subset of edges measured by others could be used to normalise the dataset post-hoc.
5.3.2. Global Multi-Waypoint Optimisation. The segment-wise chaining approach could be replaced or
supplemented by a post-processing step that detects and removes repeated edge sequences from the
concatenated route. For a small number of waypoints (as constrained by the current maximum of five), an
exhaustive check of all permutations of intermediate waypoints would also be computationally tractable,
allowing the system to find the globally optimal visit order rather than assuming the user-specified order
is optimal.
5.3.3. Time-Dependent Edge Weights. If traversal time measurements were collected at multiple times
of day (e.g., peak and off-peak), the graph model could be extended to store multiple time-dependent
weights per edge. The route engine could then select the appropriate weight based on the current time,
producing more accurate estimates during busy periods.
References
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16 C. BANH, S. LIU, J. SHING, Y. YE, AND Y. ZHANG
Appendix A. Revised Task Assignment
The workload assignment planned in the project’s plan has been revised as the project progressed.
As of the end of the project, the workload distribution by each member is declared as follows:
The contributions declared by C. Banh are:
• Survey existing navigation solutions and compare them in a table format.
• Problem modelling for our solution: definition of the type of nodes, measuring method.
• Data collection: collect data in between other team members’ assigned zones.
• Provide suggestion and recommendation of displaying UI and useful modes in the program.
• Data cleaning: cleaning up messy data in the Google Sheet.
The contributions declared by S. Liu are:
• Be responsible for data collection of a specific region, recorded the time and distance data between
several different destinations.
• Data recording: settled and arranged a part of recorded data, provided resources for coding and
modelling.
• Checking the program by verifying the accuracy through the comparison with reality, provide
related possible solutions and potential improvement for different cases.
The contributions declared by J. Shing are:
• Problem Modelling: Came up with the rough idea of modelling the campus with the common
approach of a graph. Afterwards, finalised more details on the graph representation (e.g., undi-
rected/directed graph, simple graph/multigraph, attributes that edges and nodes should have,
etc.) and implemented the fundamental data classes in Python.
• Core Implementation: Was responsible for implementing the core functionality of the Navi-
gator app, including the design of UI/UX and the code of the pathfinding engine. Apart from
coding, “implementation” also includes deciding on the technical stack, project architecture,
development workflow, etc.
• Data Collection: Responsible for collecting traversal time data for several buildings, including
Meng Wah Complex, Chong Yuet Ming Amenities Centre, Chong Yuet Ming Physics/Chemistry
Building, etc.
• Report Writing: For the final report, was responsible for writing the sections on problem
definition and implementation details.
• Typesetting: While other parts of the report were written by other members, they were written
on a shared Google Docs file. To maintain a consistent style and format across the report, and
provide a more professional presentation, was responsible for typesetting the entire report in
L
A
T
E
X.
The contributions declared by Y. Ye are:
• Collaborated on organising a list of tasks for accomplishing the project.
• Assisted with problem modelling for the solution.
• Contributed to data collection.
• Made a draft of the project timeline.
• Wrote README.md, including sections on “How to Use”, “Purposes of Each File”, and “Sample
Test Cases”.
• Executed test cases and evaluated the results.
The contributions declared by Y. Zhang are:
• Choose key places in the campus to be the nodes in our model.
• Partially collected real-world walking times between campus buildings.
• Analysed the data requirements and theoretically verified the logical feasibility of our journey-
generation approach before coding.
• Conducted edge-case testing on the Python app.
• Authored the README.md, detailing the setup, test cases, and some of the purposes of code files
in the repository.
The group has reviewed the above declared contributions and unanimously agrees that they are a fair
and accurate representation of each member’s contributions to the project.
Page 16 of 16
See also
-
COMP1110-Project
The GitHub repository of the group project.
-
COMP1110-Project-IndividualReport_JacobShing
Individual report for COMP1110 (25/26 Spring) by Jacob Shing.
-
COMP1110-Project-Plan
The project plan for COMP1110 (25/26 Spring).
-
COMP1110-Quiz
The final quiz for COMP1110 with 50 multiple-choice questions, solutions, and explanations.
-
CCCH9044-Individual-Essay
The 3000-word individual essay for the course CCCH9044.
-
CCGL9042-Question-Bank
The question bank for CCGL9042 in-class quizzes.