A new industry performance report by the Nigerian Communications Commission (NCC) shows that about 78% of Nigeria’s major roads now have mobile signalA new industry performance report by the Nigerian Communications Commission (NCC) shows that about 78% of Nigeria’s major roads now have mobile signal

Almost four in five of Nigeria’s major roads now have mobile signal, NCC says

About 78% of Nigeria’s major roads now have mobile signal, reflecting growing investment in telecom infrastructure even as coverage gaps persist, according to a new industry performance report by the Nigerian Communications Commission (NCC).

While nominal coverage is high, only around 42% of these corridors offer stable, high-speed 4G or 5G connectivity, highlighting a gap between network presence and service quality. 

Millions of Nigerians rely on mobile networks while on the move, including farmers transporting goods to urban markets, security operatives monitoring highways, and emergency services responding to accidents. 

“There’s a critical need for moving goods and general logistics, and connectivity plays a central role in that,” Umar S. Abdullahi, Special Adviser (Technical), Office of the Executive Vice Chairman of the NCC, said during the presentation of the report on Wednesday, January 28, 2026. 

That 78% of Nigeria’s major roads now have mobile connectivity matters because being connected on the move underpins safety, commerce, and daily economic activity. As millions of Nigerians travel highways to move goods, respond to emergencies, or secure key corridors, mobile networks have become as critical to national functioning as the roads themselves.

The report classified the country’s roads not by the traditional federal or state designation, but into three practical categories: trunk, primary, and secondary routes. Trunk roads connect major cities and economic corridors; primary roads serve urban centres, while secondary roads link smaller towns and rural communities. 

Using geospatial mapping and crowdsourced device data, the NCC said it assessed both signal strength and quality to determine actual service availability.

Gaps and black spots

The report identifies significant black spots where mobile connectivity is weak or nonexistent. 

Over 120 “critical black spots” were flagged, defined as highway sections longer than five kilometres with total signal loss, mostly concentrated in the North-Central and South-East regions. 

The audit also found that the average dropped-call rate along high-speed corridors stands at 4.5%, more than four times higher than the national target of less than 1%.

Abdullahi said that while the economic corridors around Lagos, Abuja, and Port Harcourt enjoy robust signals, border towns and rural areas often suffer from poor service.  He explained that primary roads have relatively better coverage, whereas secondary roads are more sporadic due to their spread across lower-density areas. 

Trunk roads, often high-speed interstate highways, face the greatest challenge in maintaining consistent signal strength due to the difficulty of sustaining coverage along long, fast-moving routes.

Infrastructure and security challenges

The report highlights several obstacles to achieving consistent mobile service along Nigeria’s highways. Vandalism is a major concern; highway base stations are three times more likely to be tampered with or have their batteries stolen than urban sites. 

Power supply is another critical issue. Over 90% of highway towers rely solely on diesel generators, making service maintenance costlier than in cities, according to Abdullahi.

Right-of-way challenges further complicate network expansion. Despite federal directives to streamline access, some states still impose high fees for laying fibre along interstate highways, delaying the deployment of essential backhaul infrastructure for 4G and 5G networks. Abdullahi added that environmental factors, such as hilly and mountainous terrain, also affect coverage, particularly in remote areas.

The NCC said it has outlined several strategic interventions for 2026. One key initiative under consideration is inter-operator roaming along highway corridors, which would allow a subscriber’s phone to automatically switch to the strongest available network in low-coverage areas. 

Dedicated highway spectrum is another priority, with the NCC planning to assign the 700 MHz and 800 MHz bands to operators that commit to end-to-end coverage on major routes. Because these low-frequency bands travel long distances and penetrate obstacles, they are well-suited to delivering consistent, wide-area mobile coverage along highways and across rural corridors.

The commission is also exploring colocation incentives for infrastructure companies (Infracos), encouraging the development of passive towers and power systems along highways to reduce costs for mobile operators.

 Impact on safety and the economy

Poor highway connectivity has direct consequences for safety and economic activity. In the event of accidents or security incidents, delayed communication can hinder emergency response, potentially worsening outcomes. 

From an economic perspective, improving connectivity on major roads is projected to boost Nigeria’s GDP by up to 0.5%, enabling real-time logistics tracking and data-driven decision-making for business travellers and transport operators, according to the commission.

Abdullahi explained that the NCC’s report provides the technical foundation for enforcement under the government’s 90-day framework. Operators that fail to resolve identified black spots within the stipulated period could face sanctions, representing a critical step in holding service providers accountable.

Performance across networks

The NCC’s analysis also compared coverage across operators. MTN emerged with the widest reach across primary, secondary, and trunk roads, followed by Airtel and T2, which are more heavily concentrated in urban areas. 

The report emphasises that while 5G networks deliver the strongest signal when available, their coverage is limited, accounting for just 13% of the national network. 4G remains the most reliable option for consistent connectivity on the move.

Crowdsourced device data provided an accurate measure of real-world performance, revealing areas where nominal coverage exists but service quality is low. Signal strength and quality were measured using standard metrics, with signals below minus 100 dBm (decibels referenced to one milliwatt (mW), considered insufficient for reliable use. The data indicate that while most roads are nominally covered, ensuring usable connectivity remains a challenge.

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Turn lengthy earnings call transcripts into one-page insights using the Financial Modeling Prep APIPhoto by Bich Tran Earnings calls are packed with insights. They tell you how a company performed, what management expects in the future, and what analysts are worried about. The challenge is that these transcripts often stretch across dozens of pages, making it tough to separate the key takeaways from the noise. With the right tools, you don’t need to spend hours reading every line. By combining the Financial Modeling Prep (FMP) API with Groq’s lightning-fast LLMs, you can transform any earnings call into a concise summary in seconds. The FMP API provides reliable access to complete transcripts, while Groq handles the heavy lifting of distilling them into clear, actionable highlights. In this article, we’ll build a Python workflow that brings these two together. You’ll see how to fetch transcripts for any stock, prepare the text, and instantly generate a one-page summary. Whether you’re tracking Apple, NVIDIA, or your favorite growth stock, the process works the same — fast, accurate, and ready whenever you are. Fetching Earnings Transcripts with FMP API The first step is to pull the raw transcript data. FMP makes this simple with dedicated endpoints for earnings calls. If you want the latest transcripts across the market, you can use the stable endpoint /stable/earning-call-transcript-latest. For a specific stock, the v3 endpoint lets you request transcripts by symbol, quarter, and year using the pattern: https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={q}&year={y}&apikey=YOUR_API_KEY here’s how you can fetch NVIDIA’s transcript for a given quarter: import requestsAPI_KEY = "your_api_key"symbol = "NVDA"quarter = 2year = 2024url = f"https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={quarter}&year={year}&apikey={API_KEY}"response = requests.get(url)data = response.json()# Inspect the keysprint(data.keys())# Access transcript contentif "content" in data[0]: transcript_text = data[0]["content"] print(transcript_text[:500]) # preview first 500 characters The response typically includes details like the company symbol, quarter, year, and the full transcript text. If you aren’t sure which quarter to query, the “latest transcripts” endpoint is the quickest way to always stay up to date. Cleaning and Preparing Transcript Data Raw transcripts from the API often include long paragraphs, speaker tags, and formatting artifacts. Before sending them to an LLM, it helps to organize the text into a cleaner structure. Most transcripts follow a pattern: prepared remarks from executives first, followed by a Q&A session with analysts. Separating these sections gives better control when prompting the model. In Python, you can parse the transcript and strip out unnecessary characters. A simple way is to split by markers such as “Operator” or “Question-and-Answer.” Once separated, you can create two blocks — Prepared Remarks and Q&A — that will later be summarized independently. This ensures the model handles each section within context and avoids missing important details. Here’s a small example of how you might start preparing the data: import re# Example: using the transcript_text we fetched earliertext = transcript_text# Remove extra spaces and line breaksclean_text = re.sub(r'\s+', ' ', text).strip()# Split sections (this is a heuristic; real-world transcripts vary slightly)if "Question-and-Answer" in clean_text: prepared, qna = clean_text.split("Question-and-Answer", 1)else: prepared, qna = clean_text, ""print("Prepared Remarks Preview:\n", prepared[:500])print("\nQ&A Preview:\n", qna[:500]) With the transcript cleaned and divided, you’re ready to feed it into Groq’s LLM. Chunking may be necessary if the text is very long. A good approach is to break it into segments of a few thousand tokens, summarize each part, and then merge the summaries in a final pass. Summarizing with Groq LLM Now that the transcript is clean and split into Prepared Remarks and Q&A, we’ll use Groq to generate a crisp one-pager. The idea is simple: summarize each section separately (for focus and accuracy), then synthesize a final brief. Prompt design (concise and factual) Use a short, repeatable template that pushes for neutral, investor-ready language: You are an equity research analyst. Summarize the following earnings call sectionfor {symbol} ({quarter} {year}). Be factual and concise.Return:1) TL;DR (3–5 bullets)2) Results vs. guidance (what improved/worsened)3) Forward outlook (specific statements)4) Risks / watch-outs5) Q&A takeaways (if present)Text:<<<{section_text}>>> Python: calling Groq and getting a clean summary Groq provides an OpenAI-compatible API. Set your GROQ_API_KEY and pick a fast, high-quality model (e.g., a Llama-3.1 70B variant). We’ll write a helper to summarize any text block, then run it for both sections and merge. import osimport textwrapimport requestsGROQ_API_KEY = os.environ.get("GROQ_API_KEY") or "your_groq_api_key"GROQ_BASE_URL = "https://api.groq.com/openai/v1" # OpenAI-compatibleMODEL = "llama-3.1-70b" # choose your preferred Groq modeldef call_groq(prompt, temperature=0.2, max_tokens=1200): url = f"{GROQ_BASE_URL}/chat/completions" headers = { "Authorization": f"Bearer {GROQ_API_KEY}", "Content-Type": "application/json", } payload = { "model": MODEL, "messages": [ {"role": "system", "content": "You are a precise, neutral equity research analyst."}, {"role": "user", "content": prompt}, ], "temperature": temperature, "max_tokens": max_tokens, } r = requests.post(url, headers=headers, json=payload, timeout=60) r.raise_for_status() return r.json()["choices"][0]["message"]["content"].strip()def build_prompt(section_text, symbol, quarter, year): template = """ You are an equity research analyst. Summarize the following earnings call section for {symbol} ({quarter} {year}). Be factual and concise. Return: 1) TL;DR (3–5 bullets) 2) Results vs. guidance (what improved/worsened) 3) Forward outlook (specific statements) 4) Risks / watch-outs 5) Q&A takeaways (if present) Text: <<< {section_text} >>> """ return textwrap.dedent(template).format( symbol=symbol, quarter=quarter, year=year, section_text=section_text )def summarize_section(section_text, symbol="NVDA", quarter="Q2", year="2024"): if not section_text or section_text.strip() == "": return "(No content found for this section.)" prompt = build_prompt(section_text, symbol, quarter, year) return call_groq(prompt)# Example usage with the cleaned splits from Section 3prepared_summary = summarize_section(prepared, symbol="NVDA", quarter="Q2", year="2024")qna_summary = summarize_section(qna, symbol="NVDA", quarter="Q2", year="2024")final_one_pager = f"""# {symbol} Earnings One-Pager — {quarter} {year}## Prepared Remarks — Key Points{prepared_summary}## Q&A Highlights{qna_summary}""".strip()print(final_one_pager[:1200]) # preview Tips that keep quality high: Keep temperature low (≈0.2) for factual tone. If a section is extremely long, chunk at ~5–8k tokens, summarize each chunk with the same prompt, then ask the model to merge chunk summaries into one section summary before producing the final one-pager. If you also fetched headline numbers (EPS/revenue, guidance) earlier, prepend them to the prompt as brief context to help the model anchor on the right outcomes. Building the End-to-End Pipeline At this point, we have all the building blocks: the FMP API to fetch transcripts, a cleaning step to structure the data, and Groq LLM to generate concise summaries. The final step is to connect everything into a single workflow that can take any ticker and return a one-page earnings call summary. The flow looks like this: Input a stock ticker (for example, NVDA). Use FMP to fetch the latest transcript. Clean and split the text into Prepared Remarks and Q&A. Send each section to Groq for summarization. Merge the outputs into a neatly formatted earnings one-pager. Here’s how it comes together in Python: def summarize_earnings_call(symbol, quarter, year, api_key, groq_key): # Step 1: Fetch transcript from FMP url = f"https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={quarter}&year={year}&apikey={api_key}" resp = requests.get(url) resp.raise_for_status() data = resp.json() if not data or "content" not in data[0]: return f"No transcript found for {symbol} {quarter} {year}" text = data[0]["content"] # Step 2: Clean and split clean_text = re.sub(r'\s+', ' ', text).strip() if "Question-and-Answer" in clean_text: prepared, qna = clean_text.split("Question-and-Answer", 1) else: prepared, qna = clean_text, "" # Step 3: Summarize with Groq prepared_summary = summarize_section(prepared, symbol, quarter, year) qna_summary = summarize_section(qna, symbol, quarter, year) # Step 4: Merge into final one-pager return f"""# {symbol} Earnings One-Pager — {quarter} {year}## Prepared Remarks{prepared_summary}## Q&A Highlights{qna_summary}""".strip()# Example runprint(summarize_earnings_call("NVDA", 2, 2024, API_KEY, GROQ_API_KEY)) With this setup, generating a summary becomes as simple as calling one function with a ticker and date. 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