Car Park Overspill Study PROJECT DOCUMENTATION FINAL

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Preliminary Phase

Scope

Our sponsor requires us to finish 5 final reports of different development sites during the preliminary phase of our project. The necessary data and infographics are prepared by the sponsor and his tea, our job scope is to compile the report using the sample template and interpret the data to generate some insights which can be helpful for LTA future planning. A typical final report contains the following components:

1. Executive Summary
2. Site Background
3. Site Characteristics
4. Site Assessment
5. Survey Findings
6. Conclusion

“Executive Summary” gives a brief overview of the report and its conclusion. “Site Background”, “Site Characteristics” and “Site Assessment” provides the basic information of the site, such as the nature, the size, the transportation availability of the site and etc. Our focus is on “Survey Findings” which uses the data collected on both weekday and weekend at the site to generate some insights. The generated insights elaborate on the vehicles traffic pattern, the human traffic during different period of the day and most importantly whether there is overspill, this helps LTA understands the utilization rate of the car park and make certain adjustments in the future.

Actual Deliverables

By the end of the preliminary phase, our group has finished 5 final reports as planned at the beginning of the project. These 5 final reports are:

• Final Report - Greenwood Ave & Hillcrest Rd
• Final Report - Vivocity
• Final Report - Frankel Ave
• Final Report - Jalan Mata Ayer
• Final Report - Yuhua Market & Hawker Centre

Phase 2

INTERIM

FINAL

Revised Methodology

We have revised our goal of Phase 2, instead of building a simulation tool with projection of a car park together with its surrounding environment such as streets and roads, now we aim to let the tool to simulate a car park inside traffic only. Considering the raw data at hand, it includes surveyed car parks inbound and outbound traffic. The data become more meaningful when we uncover the pattern of car arriving at a car park and apply the pattern into a model which simulates the traffic inside a car park only. Therefore, we redefined the Phase 2 objective, and accordingly we update the preparing data process to suit the updated needs.

Data Preparation

To help us understand the traffic situation inside a car park, we process the raw data in the following steps:

1. Based on the nature of the car park's associated site, hence, we categorized 4 sets of data - Shopping Malls, F&B Clusters, Community Clubs, and Hawker Centres. The following table shows the basic information of each category:

Site Category	Shopping Mall	Community Club	Hawker Centre	F&B Cluster
Num of Samples	17	10	10	7
Survey Periods	10am - 9pm (Weekday & Weekend)	9am - 6pm (Weekend), 2pm - 10pm (Weekday)	7am - 3pm (Weekday & Weekend), 6pm - 8pm(Weekday)	10am - 10pm (Weekday & Weekend)

2. Organize the Inbound Traffic data of each site in a site category, such that we have a collection of data showing the number of arriving cars at each car park in each 15 minutes interval from 7:00 am to 10:00 pm. The following screen shot shows an example of the collection of data of Shopping Mall Category sites:

3. Since the survey of Inbound Traffic data was collected in varied time periods in a day, there are missing data in the in some time intervals in the consolidated data set, we replace the missing data with mean value of each data row.

4. We understand that the car parks' associated development site have varied capacities in accommodating patrons or customers, so we normalized the data , by applying below formula:

The following screen shot shows an instance of data has been normalized:

5. After normalizing the data, in each site category, we still found a few outliers with normalized value deviating from the mean value greatly. To ensure that the data as input is effective in representing the general cases to the agent-based model we build, we have to remove those outliers. The following plot chart shows an example of the effect before and after removing the outliers:

Implementation of Agent-based Model

Defining agent behaviour

By studying the data by category, we can find out the distributions of the amount of cars which will travel to the sites and use the carpark. Also the chances that they may park on the street illegally. More complex behavior can be investigated if time allows. There is only a type of agent in our project which is the car travelling to the development site. The behavior of the car can be defined by few rules:

• Only cars which are travelling to the development site will be generated on the map
• Individual car in the map will travel along available roads towards the development site
• Average parking time of individual car is set to be 60min
• Cars will leave the carpark, travel towards the edge of the map and exit

There are 3 types of patches that we need to define in the Model: road, development site and carpark:

• Road patches allows cars to travel on it and there will be a “direction” variable relates to it to define the direction that the cars should follow
• Development site patches indicates the location of the site, there is no functionality relates to the patches
• Carpark patches allows cars to go in and out of the it, there will be a “count” variable which indicates the amount of cars in the carpark

Next Following-up

The next stage of the project focuses on development of the model since the major elements of the model are fully prepared which are the agents’ behavior and data.

The tools which we are going to use is Netlogo as mentioned in the project proposal. Netlogo is known to be a user-friendly tool in agent based simulation field due to its clear graphical interface, great flexibility and easy to pick up coding syntax. All simulation model in Netlogo involves 2 development stages, the first one is the “setup” function which creates the default environment that the agent is going to interact in, the other one is “go” function which triggers the agent’s action and iterates infinitely until a result is generated or the user stops the process.

Our project aims to incorporate real world elements into the model, such as the road direction and traffic condition to provide more convincing results of carpark overspill study. Therefore, “setup” function will be a challenge and also the main focus for our next stage plan. We would like to use the drawing functionalities in Netlogo to enable the user to input the settings of the environment which is close to the real world development site.

The “go” function operates the agents based on the general rule we have discussed earlier on in the agents’ behavior section. The logic of some other considerations such as traffic light, crossings and turning of the vehicles will also be coded in this part to simulate a more realistic environment.

Therefore, we believe that the interaction between the pre-coded agents in the user input environment will generate interesting and useful insights for the user.